From d46b5ce6e56ab784fddfeec3ebd7f7f280460f9e Mon Sep 17 00:00:00 2001 From: Marcilio Duarte <104692475+marcilioduarte@users.noreply.github.com> Date: Sat, 25 Jun 2022 20:40:49 -0300 Subject: [PATCH 01/18] =?UTF-8?q?Criado=20atrav=C3=A9s=20do=20Colaboratory?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- ...ece_Aluno_Marcilio_Duarte_Turma_DS38.ipynb | 184 ++++++++++++++++++ 1 file changed, 184 insertions(+) create mode 100644 Desafio_1_de_Python_Awari_Data_Sciece_Aluno_Marcilio_Duarte_Turma_DS38.ipynb diff --git a/Desafio_1_de_Python_Awari_Data_Sciece_Aluno_Marcilio_Duarte_Turma_DS38.ipynb b/Desafio_1_de_Python_Awari_Data_Sciece_Aluno_Marcilio_Duarte_Turma_DS38.ipynb new file mode 100644 index 0000000..8ecd048 --- /dev/null +++ b/Desafio_1_de_Python_Awari_Data_Sciece_Aluno_Marcilio_Duarte_Turma_DS38.ipynb @@ -0,0 +1,184 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "name": "Desafio 1 de Python - Awari_Data Sciece - Aluno: Marcilio Duarte_Turma_DS38", + "provenance": [], + "collapsed_sections": [], + "authorship_tag": "ABX9TyPjGxy2il8K34GItJLeNrOT", + "include_colab_link": true + }, + "kernelspec": { + "name": "python3", + "display_name": "Python 3" + }, + "language_info": { + "name": "python" + } + }, + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "view-in-github", + "colab_type": "text" + }, + "source": [ + "\"Open" + ] + }, + { + "cell_type": "markdown", + "source": [ + "###**Resolução do Desafio 1 de Python - Awari_Data Sciece - Aluno: Marcilio Duarte_Turma_DS38**" + ], + "metadata": { + "id": "dlI3SegUDwzM" + } + }, + { + "cell_type": "markdown", + "source": [ + "### Desafio 1\n", + "\n", + "Escreva um programa em Python para contabilizar a quantidade de ocorrências de cada palavra.\n" + ], + "metadata": { + "id": "EVfe6n01EzIk" + } + }, + { + "cell_type": "code", + "source": [ + "palavras = [\n", + " 'red', 'green', 'black', 'pink', 'black', 'white', 'black', 'eyes',\n", + " 'white', 'black', 'orange', 'pink', 'pink', 'red', 'red', 'white', 'orange',\n", + " 'white', \"black\", 'pink', 'green', 'green', 'pink', 'green', 'pink',\n", + " 'white', 'orange', \"orange\", 'red'\n", + "]" + ], + "metadata": { + "id": "P6bYkr3eE4dK" + }, + "execution_count": 2, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "#Opção 1:\n", + "from collections import Counter\n", + "dicionario = Counter(palavras)\n", + "dicionario" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "aLxpqRYMFyAL", + "outputId": "27ba8b8b-3c23-4a47-eda8-b36a90b81c67" + }, + "execution_count": 8, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "Counter({'black': 5,\n", + " 'eyes': 1,\n", + " 'green': 4,\n", + " 'orange': 4,\n", + " 'pink': 6,\n", + " 'red': 4,\n", + " 'white': 5})" + ] + }, + "metadata": {}, + "execution_count": 8 + } + ] + }, + { + "cell_type": "code", + "source": [ + "#Opção 2:\n", + "contagem={}\n", + "for n in palavras:\n", + " total=palavras.count(n)\n", + " contagem[f'{n}']=total\n", + "print(contagem)" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "Q2fiDUNYGgZ_", + "outputId": "03872bae-d829-4104-fe96-731f84f301f7" + }, + "execution_count": 9, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "{'red': 4, 'green': 4, 'black': 5, 'pink': 6, 'white': 5, 'eyes': 1, 'orange': 4}\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "#Opção 3:\n", + "for n in palavras:\n", + " print (n + \" \" + str(dicionario[n]) if n in palavras else 0)" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "pftwYNeuH-bb", + "outputId": "51632af5-d717-40e4-83e6-8d23191b23e4" + }, + "execution_count": 10, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "red 4\n", + "green 4\n", + "black 5\n", + "pink 6\n", + "black 5\n", + "white 5\n", + "black 5\n", + "eyes 1\n", + "white 5\n", + "black 5\n", + "orange 4\n", + "pink 6\n", + "pink 6\n", + "red 4\n", + "red 4\n", + "white 5\n", + "orange 4\n", + "white 5\n", + "black 5\n", + "pink 6\n", + "green 4\n", + "green 4\n", + "pink 6\n", + "green 4\n", + "pink 6\n", + "white 5\n", + "orange 4\n", + "orange 4\n", + "red 4\n" + ] + } + ] + } + ] +} \ No newline at end of file From beb0df967b7f0b1875beeee731dd507ca4c137ff Mon Sep 17 00:00:00 2001 From: Marcilio Duarte <104692475+marcilioduarte@users.noreply.github.com> Date: Sat, 25 Jun 2022 22:01:29 -0300 Subject: [PATCH 02/18] =?UTF-8?q?Criado=20atrav=C3=A9s=20do=20Colaboratory?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- ...ece_Aluno_Marcilio_Duarte_Turma_DS38.ipynb | 91 +++++++++++++++++++ 1 file changed, 91 insertions(+) create mode 100644 Desafio_2_de_Python_Awari_Data_Sciece_Aluno_Marcilio_Duarte_Turma_DS38.ipynb diff --git a/Desafio_2_de_Python_Awari_Data_Sciece_Aluno_Marcilio_Duarte_Turma_DS38.ipynb b/Desafio_2_de_Python_Awari_Data_Sciece_Aluno_Marcilio_Duarte_Turma_DS38.ipynb new file mode 100644 index 0000000..38c2ee3 --- /dev/null +++ b/Desafio_2_de_Python_Awari_Data_Sciece_Aluno_Marcilio_Duarte_Turma_DS38.ipynb @@ -0,0 +1,91 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "name": "Desafio 2 de Python - Awari_Data Sciece - Aluno: Marcilio Duarte_Turma_DS38", + "provenance": [], + "collapsed_sections": [], + "authorship_tag": "ABX9TyPt9QtN37Aa0mwcbG+2QpnU", + "include_colab_link": true + }, + "kernelspec": { + "name": "python3", + "display_name": "Python 3" + }, + "language_info": { + "name": "python" + } + }, + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "view-in-github", + "colab_type": "text" + }, + "source": [ + "\"Open" + ] + }, + { + "cell_type": "markdown", + "source": [ + "![](https://i.imgur.com/YX6UATs.png)" + ], + "metadata": { + "id": "FrFjUlLrcTDh" + } + }, + { + "cell_type": "markdown", + "source": [ + "\n", + "### Desafio 2\n", + "\n", + "Escreva uma função que receba um número inteiro de horas e converta esse número para segundos.\n", + "\n", + "Exemplo:\n", + "\n", + "convert(5) ➞ 18000\n", + "\n", + "convert(3) ➞ 10800\n", + "\n", + "convert(2) ➞ 7200\n", + "\n" + ], + "metadata": { + "id": "BkVaOex-bcX4" + } + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "5K3W-K-sbaRU", + "outputId": "a53dc8c1-3df4-4950-c348-dd3ce52b6584" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "3600\n", + "7200\n" + ] + } + ], + "source": [ + "#opção 1:\n", + "def convert_to_sec(number):\n", + " x=number * 3600\n", + " print(x)\n", + "convert_to_sec(1)\n", + "convert_to_sec(2)" + ] + } + ] +} \ No newline at end of file From a29c2e3ffe8074813caee79974b28a614aebc71d Mon Sep 17 00:00:00 2001 From: Marcilio Duarte <104692475+marcilioduarte@users.noreply.github.com> Date: Sun, 26 Jun 2022 16:25:39 -0300 Subject: [PATCH 03/18] =?UTF-8?q?Criado=20atrav=C3=A9s=20do=20Colaboratory?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- ...ece_Aluno_Marcilio_Duarte_Turma_DS38.ipynb | 125 ++++++++++++++++++ 1 file changed, 125 insertions(+) create mode 100644 Desafio_3_de_Python_Awari_Data_Sciece_Aluno_Marcilio_Duarte_Turma_DS38.ipynb diff --git a/Desafio_3_de_Python_Awari_Data_Sciece_Aluno_Marcilio_Duarte_Turma_DS38.ipynb b/Desafio_3_de_Python_Awari_Data_Sciece_Aluno_Marcilio_Duarte_Turma_DS38.ipynb new file mode 100644 index 0000000..d3cd10c --- /dev/null +++ b/Desafio_3_de_Python_Awari_Data_Sciece_Aluno_Marcilio_Duarte_Turma_DS38.ipynb @@ -0,0 +1,125 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "name": "Desafio 3 de Python - Awari_Data Sciece - Aluno: Marcilio Duarte_Turma_DS38", + "provenance": [], + "authorship_tag": "ABX9TyO2MtgEsUX2sPasum3YeP9b", + "include_colab_link": true + }, + "kernelspec": { + "name": "python3", + "display_name": "Python 3" + }, + "language_info": { + "name": "python" + } + }, + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "view-in-github", + "colab_type": "text" + }, + "source": [ + "\"Open" + ] + }, + { + "cell_type": "markdown", + "source": [ + "![](https://i.imgur.com/YX6UATs.png)" + ], + "metadata": { + "id": "4JRUfqL_Z-Ht" + } + }, + { + "cell_type": "markdown", + "source": [ + "### Desafio 3\n", + "\n", + "Escreva uma função que receba uma lista como entrada e retorne uma nova lista ordenada e sem valores duplicados." + ], + "metadata": { + "id": "x317kt0oZ95x" + } + }, + { + "cell_type": "code", + "source": [ + "lista = [1,2,3,4,3,30,3,4,5,6,9,3,2,1,2,4,5,15,6,6,3,13,4,45,5]\n", + "\n", + "# Seu código" + ], + "metadata": { + "id": "L8kOFW4MaDje" + }, + "execution_count": 3, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "#opção 1:\n", + "\n", + "def lista_ordenada(n):\n", + " x=set(n)\n", + " y=sorted(x)\n", + " return list(x)\n", + "lista_ordenada(lista)\n" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "I_LZdkyraGEy", + "outputId": "67741efd-663d-4601-bd78-d7c1db24e26d" + }, + "execution_count": 9, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "[1, 2, 3, 4, 5, 6, 9, 13, 45, 15, 30]" + ] + }, + "metadata": {}, + "execution_count": 9 + } + ] + }, + { + "cell_type": "code", + "source": [ + "#opção 2:\n", + "\n", + "from collections import OrderedDict\n", + "def lista_ordenada(n):\n", + " final_list = list(OrderedDict.fromkeys(lista))\n", + " print(final_list)\n", + "lista_ordenada(lista)" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "YyyixjZ9cCKd", + "outputId": "7f3115da-55aa-40fd-dd1a-292e1c64f1ae" + }, + "execution_count": 10, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "[1, 2, 3, 4, 30, 5, 6, 9, 15, 13, 45]\n" + ] + } + ] + } + ] +} \ No newline at end of file From 8e47dac664fc7f21b5f7372a9f132be9c1a88911 Mon Sep 17 00:00:00 2001 From: Marcilio Duarte <104692475+marcilioduarte@users.noreply.github.com> Date: Sun, 26 Jun 2022 16:55:24 -0300 Subject: [PATCH 04/18] =?UTF-8?q?Criado=20atrav=C3=A9s=20do=20Colaboratory?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- ...ece_Aluno_Marcilio_Duarte_Turma_DS38.ipynb | 82 +++++++++++++++++++ 1 file changed, 82 insertions(+) create mode 100644 Desafio_4_de_Python_Awari_Data_Sciece_Aluno_Marcilio_Duarte_Turma_DS38.ipynb diff --git a/Desafio_4_de_Python_Awari_Data_Sciece_Aluno_Marcilio_Duarte_Turma_DS38.ipynb b/Desafio_4_de_Python_Awari_Data_Sciece_Aluno_Marcilio_Duarte_Turma_DS38.ipynb new file mode 100644 index 0000000..2b866c3 --- /dev/null +++ b/Desafio_4_de_Python_Awari_Data_Sciece_Aluno_Marcilio_Duarte_Turma_DS38.ipynb @@ -0,0 +1,82 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "name": "Desafio 4 de Python - Awari_Data Sciece - Aluno: Marcilio Duarte_Turma_DS38", + "provenance": [], + "authorship_tag": "ABX9TyMTOwDCgFfFshjPFNAtLafb", + "include_colab_link": true + }, + "kernelspec": { + "name": "python3", + "display_name": "Python 3" + }, + "language_info": { + "name": "python" + } + }, + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "view-in-github", + "colab_type": "text" + }, + "source": [ + "\"Open" + ] + }, + { + "cell_type": "markdown", + "source": [ + "![](https://i.imgur.com/YX6UATs.png)" + ], + "metadata": { + "id": "zJMbmQmzfgfm" + } + }, + { + "cell_type": "markdown", + "source": [ + "##Desafio 4\n", + "\n", + "Escreva uma função cuja entrada é uma string e a saída é outra string com as palavras em ordem inversa.\n", + "\n", + "Exemplo:\n", + "\n", + "inverte_texto(\"Python é legal\") ➞ \"legal é Python\"" + ], + "metadata": { + "id": "bo-dCPFwkpJ1" + } + }, + { + "cell_type": "code", + "source": [ + "import re\n", + "def inverter(frase, regex=re.compile(r\"[^\\-,.?!\\s]+\")):\n", + " return \" \".join(re.findall(regex, frase)[::-1])\n", + "\n", + "print(inverter(\"Eu roubei nessa questão porque não consegui resolver sozinho. Por isso, colei da internet.\"))\n" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "QDU3b--Bfuzr", + "outputId": "fd24c1c7-6407-4f44-d8c7-cf10f1c996f4" + }, + "execution_count": 5, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "internet da colei isso Por sozinho resolver consegui não porque questão nessa roubei Eu\n" + ] + } + ] + } + ] +} \ No newline at end of file From 271f88c41b6c5410318b25f330cd447d84015f28 Mon Sep 17 00:00:00 2001 From: Marcilio Duarte <104692475+marcilioduarte@users.noreply.github.com> Date: Sun, 26 Jun 2022 17:40:05 -0300 Subject: [PATCH 05/18] =?UTF-8?q?Criado=20atrav=C3=A9s=20do=20Colaboratory?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- ...ece_Aluno_Marcilio_Duarte_Turma_DS38.ipynb | 124 ++++++++++++++++++ 1 file changed, 124 insertions(+) create mode 100644 Desafio_5_de_Python_Awari_Data_Sciece_Aluno_Marcilio_Duarte_Turma_DS38.ipynb diff --git a/Desafio_5_de_Python_Awari_Data_Sciece_Aluno_Marcilio_Duarte_Turma_DS38.ipynb b/Desafio_5_de_Python_Awari_Data_Sciece_Aluno_Marcilio_Duarte_Turma_DS38.ipynb new file mode 100644 index 0000000..f61722a --- /dev/null +++ b/Desafio_5_de_Python_Awari_Data_Sciece_Aluno_Marcilio_Duarte_Turma_DS38.ipynb @@ -0,0 +1,124 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "name": "Desafio 5 de Python - Awari_Data Sciece - Aluno: Marcilio Duarte_Turma_DS38", + "provenance": [], + "authorship_tag": "ABX9TyOw09L0xp3SnXsptfXgoBDP", + "include_colab_link": true + }, + "kernelspec": { + "name": "python3", + "display_name": "Python 3" + }, + "language_info": { + "name": "python" + } + }, + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "view-in-github", + "colab_type": "text" + }, + "source": [ + "\"Open" + ] + }, + { + "cell_type": "markdown", + "source": [ + "![](https://i.imgur.com/YX6UATs.png)" + ], + "metadata": { + "id": "iqRQC7xmmSbp" + } + }, + { + "cell_type": "markdown", + "source": [ + "# Desafio 5\n", + "Você trabalha em uma loja de sapatos e deve contatar uma série de clientes. Seus números de telefone estão na lista abaixo. No entanto, é possível notar números duplicados na lista dada. Você seria capaz de remover estes duplicados para evitar que clientes sejam contatados mais de uma vez?\n" + ], + "metadata": { + "id": "3zAOo1pDsM11" + } + }, + { + "cell_type": "code", + "source": [ + "numeros_telefone = [\n", + "'(765) 368-1506',\n", + "'(285) 608-2448',\n", + "'(255) 826-9050',\n", + "'(554) 994-1517',\n", + "'(285) 608-2448',\n", + "'(596) 336-5508',\n", + "'(511) 821-7870',\n", + "'(410) 665-4447',\n", + "'(821) 642-8987',\n", + "'(285) 608-2448',\n", + "'(311) 799-3883',\n", + "'(935) 875-2054',\n", + "'(464) 788-2397',\n", + "'(765) 368-1506',\n", + "'(650) 684-1437',\n", + "'(812) 816-0881',\n", + "'(285) 608-2448',\n", + "'(885) 407-1719',\n", + "'(943) 769-1061',\n", + "'(596) 336-5508',\n", + "'(765) 368-1506',\n", + "'(255) 826-9050',\n", + "]" + ], + "metadata": { + "id": "qecWje2MsL7L" + }, + "execution_count": 2, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "set(sorted(numeros_telefone))" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "mhoHYiADsL22", + "outputId": "a5a63fcf-196e-4063-a132-aaf7f91d8cfc" + }, + "execution_count": 4, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "{'(255) 826-9050',\n", + " '(285) 608-2448',\n", + " '(311) 799-3883',\n", + " '(410) 665-4447',\n", + " '(464) 788-2397',\n", + " '(511) 821-7870',\n", + " '(554) 994-1517',\n", + " '(596) 336-5508',\n", + " '(650) 684-1437',\n", + " '(765) 368-1506',\n", + " '(812) 816-0881',\n", + " '(821) 642-8987',\n", + " '(885) 407-1719',\n", + " '(935) 875-2054',\n", + " '(943) 769-1061'}" + ] + }, + "metadata": {}, + "execution_count": 4 + } + ] + } + ] +} \ No newline at end of file From 9a0384825e3cbc39128d32eb94faab87eb48700e Mon Sep 17 00:00:00 2001 From: Marcilio Duarte <104692475+marcilioduarte@users.noreply.github.com> Date: Sun, 26 Jun 2022 17:42:48 -0300 Subject: [PATCH 06/18] =?UTF-8?q?Criado=20atrav=C3=A9s=20do=20Colaboratory?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- ..._Sciece_Aluno_Marcilio_Duarte_Turma_DS38.ipynb | 15 ++++++++------- 1 file changed, 8 insertions(+), 7 deletions(-) diff --git a/Desafio_5_de_Python_Awari_Data_Sciece_Aluno_Marcilio_Duarte_Turma_DS38.ipynb b/Desafio_5_de_Python_Awari_Data_Sciece_Aluno_Marcilio_Duarte_Turma_DS38.ipynb index f61722a..a5c4a46 100644 --- a/Desafio_5_de_Python_Awari_Data_Sciece_Aluno_Marcilio_Duarte_Turma_DS38.ipynb +++ b/Desafio_5_de_Python_Awari_Data_Sciece_Aluno_Marcilio_Duarte_Turma_DS38.ipynb @@ -5,7 +5,7 @@ "colab": { "name": "Desafio 5 de Python - Awari_Data Sciece - Aluno: Marcilio Duarte_Turma_DS38", "provenance": [], - "authorship_tag": "ABX9TyOw09L0xp3SnXsptfXgoBDP", + "authorship_tag": "ABX9TyPfswusC8qhDvSGedzVilRd", "include_colab_link": true }, "kernelspec": { @@ -83,22 +83,23 @@ { "cell_type": "code", "source": [ - "set(sorted(numeros_telefone))" + "numeros_telefone_novo=list(sorted(set(numeros_telefone)))\n", + "numeros_telefone_novo" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "mhoHYiADsL22", - "outputId": "a5a63fcf-196e-4063-a132-aaf7f91d8cfc" + "outputId": "82b832be-3393-4f32-d087-3f01628595f4" }, - "execution_count": 4, + "execution_count": 8, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ - "{'(255) 826-9050',\n", + "['(255) 826-9050',\n", " '(285) 608-2448',\n", " '(311) 799-3883',\n", " '(410) 665-4447',\n", @@ -112,11 +113,11 @@ " '(821) 642-8987',\n", " '(885) 407-1719',\n", " '(935) 875-2054',\n", - " '(943) 769-1061'}" + " '(943) 769-1061']" ] }, "metadata": {}, - "execution_count": 4 + "execution_count": 8 } ] } From efea8467a880a9f5b5edb27913abf8b99c487b0f Mon Sep 17 00:00:00 2001 From: Marcilio Duarte <104692475+marcilioduarte@users.noreply.github.com> Date: Sun, 26 Jun 2022 18:09:03 -0300 Subject: [PATCH 07/18] =?UTF-8?q?Criado=20atrav=C3=A9s=20do=20Colaboratory?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- ...ece_Aluno_Marcilio_Duarte_Turma_DS38.ipynb | 197 ++++++++++++++++++ 1 file changed, 197 insertions(+) create mode 100644 Desafio_6_de_Python_Awari_Data_Sciece_Aluno_Marcilio_Duarte_Turma_DS38.ipynb diff --git a/Desafio_6_de_Python_Awari_Data_Sciece_Aluno_Marcilio_Duarte_Turma_DS38.ipynb b/Desafio_6_de_Python_Awari_Data_Sciece_Aluno_Marcilio_Duarte_Turma_DS38.ipynb new file mode 100644 index 0000000..0eb30d9 --- /dev/null +++ b/Desafio_6_de_Python_Awari_Data_Sciece_Aluno_Marcilio_Duarte_Turma_DS38.ipynb @@ -0,0 +1,197 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "name": "Desafio 6 de Python - Awari_Data Sciece - Aluno: Marcilio Duarte_Turma_DS38", + "provenance": [], + "authorship_tag": "ABX9TyPHjkLGeAUAA4p8PbI9Arc0", + "include_colab_link": true + }, + "kernelspec": { + "name": "python3", + "display_name": "Python 3" + }, + "language_info": { + "name": "python" + } + }, + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "view-in-github", + "colab_type": "text" + }, + "source": [ + "\"Open" + ] + }, + { + "cell_type": "markdown", + "source": [ + "![](https://i.imgur.com/YX6UATs.png)" + ], + "metadata": { + "id": "3YJTe3gaw4CW" + } + }, + { + "cell_type": "markdown", + "source": [ + "### Desafio 6\n", + "\n", + "Crie uma função que receba duas listas e retorne uma lista que contenha apenas os elementos comuns entre as listas (sem repetição). A função deve suportar lista de tamanhos diferentes.\n" + ], + "metadata": { + "id": "QFyhVdGjw8Jv" + } + }, + { + "cell_type": "markdown", + "source": [ + "**Listas**" + ], + "metadata": { + "id": "nq5U0jf5xATE" + } + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": { + "id": "iRBV_p97wex0" + }, + "outputs": [], + "source": [ + "a = [1, 1, 2, 3, 5, 8, 13, 21, 34, 55, 89]\n", + "b = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13]" + ] + }, + { + "cell_type": "code", + "source": [ + "#opção 1:\n", + "\n", + "def join_listas (x,y):\n", + " z=list(sorted(set([*x, *y])))\n", + " return z\n", + "join_listas(a,b)\n" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "2UGGOdqExJAJ", + "outputId": "dc5d6604-88b0-4d8b-ab03-cc27a323b8db" + }, + "execution_count": 20, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 21, 34, 55, 89]" + ] + }, + "metadata": {}, + "execution_count": 20 + } + ] + }, + { + "cell_type": "code", + "source": [ + "#opção 2:\n", + "\n", + "def join_listas (x,y):\n", + " z=x+y\n", + " return sorted(set(z))\n", + "join_listas(a,b)\n" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "y1lLTKM71BN_", + "outputId": "7f89dd8e-b372-48ee-9e5e-e5960d2f3ee1" + }, + "execution_count": 21, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 21, 34, 55, 89]" + ] + }, + "metadata": {}, + "execution_count": 21 + } + ] + }, + { + "cell_type": "code", + "source": [ + "#opção 3:\n", + "\n", + "def join_listas (x,y):\n", + " for i in y: \n", + " x.append(i)\n", + " return sorted(set(x))\n", + "join_listas(a,b)" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "kSi-G-wBzWVs", + "outputId": "d314b80c-b813-4c7c-ebf3-dbc9d6b026d0" + }, + "execution_count": 22, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 21, 34, 55, 89]" + ] + }, + "metadata": {}, + "execution_count": 22 + } + ] + }, + { + "cell_type": "code", + "source": [ + "#opção 4:\n", + "import itertools \n", + "def join_listas(x,y):\n", + " z = list(itertools.chain(x,y))\n", + " return sorted(set(z))\n", + "join_listas(a,b)" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "6GpfdfMs1nca", + "outputId": "e53bf4ee-c40e-47ed-bec9-336054961239" + }, + "execution_count": 24, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 21, 34, 55, 89]" + ] + }, + "metadata": {}, + "execution_count": 24 + } + ] + } + ] +} \ No newline at end of file From 7fa46c65b3f4b9e86423d96e511ce83055db7a58 Mon Sep 17 00:00:00 2001 From: Marcilio Duarte <104692475+marcilioduarte@users.noreply.github.com> Date: Sun, 26 Jun 2022 18:34:17 -0300 Subject: [PATCH 08/18] =?UTF-8?q?Criado=20atrav=C3=A9s=20do=20Colaboratory?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- ...ece_Aluno_Marcilio_Duarte_Turma_DS38.ipynb | 151 ++++++++++++++++++ 1 file changed, 151 insertions(+) create mode 100644 Desafio_7_de_Python_Awari_Data_Sciece_Aluno_Marcilio_Duarte_Turma_DS38.ipynb diff --git a/Desafio_7_de_Python_Awari_Data_Sciece_Aluno_Marcilio_Duarte_Turma_DS38.ipynb b/Desafio_7_de_Python_Awari_Data_Sciece_Aluno_Marcilio_Duarte_Turma_DS38.ipynb new file mode 100644 index 0000000..210c242 --- /dev/null +++ b/Desafio_7_de_Python_Awari_Data_Sciece_Aluno_Marcilio_Duarte_Turma_DS38.ipynb @@ -0,0 +1,151 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "name": "Desafio 7 de Python - Awari_Data Sciece - Aluno: Marcilio Duarte_Turma_DS38", + "provenance": [], + "authorship_tag": "ABX9TyP+1YmAveHsugizZ7s2pmPu", + "include_colab_link": true + }, + "kernelspec": { + "name": "python3", + "display_name": "Python 3" + }, + "language_info": { + "name": "python" + } + }, + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "view-in-github", + "colab_type": "text" + }, + "source": [ + "\"Open" + ] + }, + { + "cell_type": "markdown", + "source": [ + "![](https://i.imgur.com/YX6UATs.png)" + ], + "metadata": { + "id": "6p6QOo884QOI" + } + }, + { + "cell_type": "markdown", + "source": [ + "# Desafio 7\n", + "Um professor de universidade tem uma turma com os seguintes números de telefones:" + ], + "metadata": { + "id": "ic900xT74VZf" + } + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "id": "MZSGneCT4OmF" + }, + "outputs": [], + "source": [ + "telefones_alunos = ['(873) 810-8267', '(633) 244-7325', '(300) 303-5462', \n", + " '(938) 300-8890', '(429) 264-7427', '(737) 805-2326', \n", + " '(768) 956-8497', '(941) 225-3869', '(203) 606-9463', \n", + " '(294) 430-7720', '(896) 781-5087', '(397) 845-8267', \n", + " '(788) 717-6858', '(419) 734-4188', '(682) 595-3278', \n", + " '(835) 955-1498', '(296) 415-9944', '(897) 932-2512', \n", + " '(263) 415-3893', '(822) 640-8496', '(640) 427-2597', \n", + " '(856) 338-7094', '(807) 554-4076', '(641) 367-5279', \n", + " '(828) 866-0696', '(727) 376-5749', '(921) 948-2244', \n", + " '(964) 710-9625', '(596) 685-1242', '(403) 343-7705', \n", + " '(227) 389-3685', '(264) 372-7298', '(797) 649-3653', \n", + " '(374) 361-3844', '(618) 490-4228', '(987) 803-5550', \n", + " '(228) 976-9699', '(757) 450-9985', '(491) 666-5367',\n", + " ]" + ] + }, + { + "cell_type": "markdown", + "source": [ + "Ele criou um grupo do WhatsApp. No entanto, somente os seguintes números entraram no grupo:" + ], + "metadata": { + "id": "Y1HqZuT04kEU" + } + }, + { + "cell_type": "code", + "source": [ + "entraram_no_grupo = ['(596) 685-1242', '(727) 376-5749', '(987) 803-5550', \n", + " '(633) 244-7325', '(828) 866-0696', '(263) 415-3893', \n", + " '(203) 606-9463', '(296) 415-9944', '(419) 734-4188', \n", + " '(618) 490-4228', '(682) 595-3278', '(938) 300-8890', \n", + " '(264) 372-7298', '(768) 956-8497', '(737) 805-2326', \n", + " '(788) 717-6858', '(228) 976-9699', '(896) 781-5087',\n", + " '(374) 361-3844', '(921) 948-2244', '(807) 554-4076', \n", + " '(822) 640-8496', '(227) 389-3685', '(429) 264-7427', \n", + " '(397) 845-8267']" + ], + "metadata": { + "id": "jB6gwIub4qc9" + }, + "execution_count": 2, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "Você seria capaz de criar uma lista dos alunos que ainda não entraram no grupo para que sejam contatados individualmente?" + ], + "metadata": { + "id": "e0VxCfgA43qC" + } + }, + { + "cell_type": "code", + "source": [ + "from pprint import pprint\n", + "alunos = set(telefones_alunos)\n", + "alunos_grupo = set(entraram_no_grupo)\n", + "alunos_fora_grupo = sorted(list(alunos.difference(alunos_grupo)))\n", + "pprint(alunos_fora_grupo)" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "IzvGkMlT46fM", + "outputId": "28bdb93a-5ced-45d1-e17f-8f399097e42b" + }, + "execution_count": 6, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "['(294) 430-7720',\n", + " '(300) 303-5462',\n", + " '(403) 343-7705',\n", + " '(491) 666-5367',\n", + " '(640) 427-2597',\n", + " '(641) 367-5279',\n", + " '(757) 450-9985',\n", + " '(797) 649-3653',\n", + " '(835) 955-1498',\n", + " '(856) 338-7094',\n", + " '(873) 810-8267',\n", + " '(897) 932-2512',\n", + " '(941) 225-3869',\n", + " '(964) 710-9625']\n" + ] + } + ] + } + ] +} \ No newline at end of file From 89f1205cc9453d22e1d75f7b52d5cd075af0ef8d Mon Sep 17 00:00:00 2001 From: Marcilio Duarte <104692475+marcilioduarte@users.noreply.github.com> Date: Sat, 9 Jul 2022 17:14:21 -0300 Subject: [PATCH 09/18] =?UTF-8?q?Criado=20atrav=C3=A9s=20do=20Colaboratory?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- ...ece_Aluno_Marcilio_Duarte_Turma_DS38.ipynb | 158 ++++++++++++++++++ 1 file changed, 158 insertions(+) create mode 100644 Desafio_10_de_Python_Awari_Data_Sciece_Aluno_Marcilio_Duarte_Turma_DS38.ipynb diff --git a/Desafio_10_de_Python_Awari_Data_Sciece_Aluno_Marcilio_Duarte_Turma_DS38.ipynb b/Desafio_10_de_Python_Awari_Data_Sciece_Aluno_Marcilio_Duarte_Turma_DS38.ipynb new file mode 100644 index 0000000..e783577 --- /dev/null +++ b/Desafio_10_de_Python_Awari_Data_Sciece_Aluno_Marcilio_Duarte_Turma_DS38.ipynb @@ -0,0 +1,158 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "name": "Desafio 10 de Python - Awari_Data Sciece - Aluno: Marcilio Duarte_Turma_DS38", + "provenance": [], + "authorship_tag": "ABX9TyM0o5DAV9vjIdntuusS8UH1", + "include_colab_link": true + }, + "kernelspec": { + "name": "python3", + "display_name": "Python 3" + }, + "language_info": { + "name": "python" + } + }, + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "view-in-github", + "colab_type": "text" + }, + "source": [ + "\"Open" + ] + }, + { + "cell_type": "markdown", + "source": [ + "![](https://i.imgur.com/YX6UATs.png)" + ], + "metadata": { + "id": "mZ7eDfdeJpXc" + } + }, + { + "cell_type": "markdown", + "source": [ + "### Desafio 10\n", + "\n", + "Dada uma lista, divida-a em 3 partes iguais e reverta a ordem de cada lista.\n", + "\n", + "**Exemplo:** \n", + "\n", + "Entrada: \\\n", + "sampleList = [11, 45, 8, 23, 14, 12, 78, 45, 89]\n", + "\n", + "Saída: \\\n", + "Parte 1 [8, 45, 11] \\\n", + "Parte 2 [12, 14, 23] \\\n", + "Parte 3 [89, 45, 78] " + ], + "metadata": { + "id": "U5fw_LumJpEU" + } + }, + { + "cell_type": "code", + "source": [ + "sampleList = [11, 45, 8, 23, 14, 12, 78, 45, 89]" + ], + "metadata": { + "id": "hccSagwTJx-9" + }, + "execution_count": 1, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "#Solução 1:\n", + "\n", + "n = 3\n", + "def Parte_1(lst, n): \n", + " for i in range(0, len(sampleList), n): \n", + " return list(reversed(lst[i:i + n]))\n", + "def Parte_2(lst, n):\n", + " for i in range(3,3+n,n):\n", + " return list(reversed(lst[i:i+n]))\n", + "def Parte_3(lst, n):\n", + " for i in range(6,6+n,n):\n", + " return list(reversed(lst[i:i+n]))\n", + "\n", + "print(f'Parte 1: {(Parte_1(sampleList, n))}'\n", + " f'\\nParte 2: {(Parte_2(sampleList, n))}'\n", + " f'\\nParte 3: {(Parte_3(sampleList, n))}')" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "mTTuOT57aG3d", + "outputId": "9b336f30-547c-4ec6-e17c-bb8a692e2858" + }, + "execution_count": 31, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Parte 1: [8, 45, 11]\n", + "Parte 2: [12, 14, 23]\n", + "Parte 3: [89, 45, 78]\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "#Solução 2: resposta da awari\n", + "l1 = []\n", + "l2 = []\n", + "l3 = []\n", + "qtde = len(sampleList) / 3\n", + "if qtde.is_integer():\n", + " for elem in sampleList:\n", + " if len(l1) < qtde:\n", + " l1.append(elem)\n", + " parte_1 = reversed(l1)\n", + " elif len(l2) < qtde:\n", + " l2.append(elem)\n", + " parte_2 = reversed(l2)\n", + " else:\n", + " if len(l3) < qtde:\n", + " l3.append(elem)\n", + " parte_3 = reversed(l3)\n", + " print(f'Parte 1: {list(parte_1)}'\n", + " f'\\nParte 2: {list(parte_2)}'\n", + " f'\\nParte 3: {list(parte_3)}')\n", + "else:\n", + " print('A respectiva lista não pode ser dividida em 3 partes iguais!')" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "VCbwsZ2VehKA", + "outputId": "007f8353-6b1f-40c8-c578-f734941ab880" + }, + "execution_count": 25, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Parte 1: [8, 45, 11]\n", + "Parte 2: [12, 14, 23]\n", + "Parte 3: [89, 45, 78]\n" + ] + } + ] + } + ] +} \ No newline at end of file From 13086a4af7fae2cd2acd6791b16064bccea2f3d4 Mon Sep 17 00:00:00 2001 From: Marcilio Duarte <104692475+marcilioduarte@users.noreply.github.com> Date: Sat, 9 Jul 2022 18:08:20 -0300 Subject: [PATCH 10/18] =?UTF-8?q?Criado=20atrav=C3=A9s=20do=20Colaboratory?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- ...ece_Aluno_Marcilio_Duarte_Turma_DS38.ipynb | 94 +++++++++++++++++++ 1 file changed, 94 insertions(+) create mode 100644 Desafio_11_de_Python_Awari_Data_Sciece_Aluno_Marcilio_Duarte_Turma_DS38.ipynb diff --git a/Desafio_11_de_Python_Awari_Data_Sciece_Aluno_Marcilio_Duarte_Turma_DS38.ipynb b/Desafio_11_de_Python_Awari_Data_Sciece_Aluno_Marcilio_Duarte_Turma_DS38.ipynb new file mode 100644 index 0000000..421dd62 --- /dev/null +++ b/Desafio_11_de_Python_Awari_Data_Sciece_Aluno_Marcilio_Duarte_Turma_DS38.ipynb @@ -0,0 +1,94 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "name": "Desafio 11 de Python - Awari_Data Sciece - Aluno: Marcilio Duarte_Turma_DS38", + "provenance": [], + "authorship_tag": "ABX9TyP/y9wswfpfNMfkO89d/P9c", + "include_colab_link": true + }, + "kernelspec": { + "name": "python3", + "display_name": "Python 3" + }, + "language_info": { + "name": "python" + } + }, + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "view-in-github", + "colab_type": "text" + }, + "source": [ + "\"Open" + ] + }, + { + "cell_type": "markdown", + "source": [ + "![](https://i.imgur.com/YX6UATs.png)" + ], + "metadata": { + "id": "Khf4Hipaom-h" + } + }, + { + "cell_type": "markdown", + "source": [ + "### Desafio 11\n", + "Dados uma sequência com `n` números inteiros, determinar quantos números da sequência são pares e quantos são ímpares.\\\n", + "Por exemplo, para a sequência\n", + "\n", + "`6 2 7 -5 8 -4`\n", + "\n", + "a sua função deve retornar o número 4 para o número de pares e 2 para o de ímpares.\\\n", + "A saída deve ser um **tupla** contendo primeiramente o número de pares e em seguida o número de ímpares.\\\n", + "Para o exemplo anterior, a saída seria `(4, 2)`." + ], + "metadata": { + "id": "_NGs9dWWodso" + } + }, + { + "cell_type": "code", + "source": [ + "#solução 1:\n", + "lista_aleatoria=[22,24,26,27,29,31,33]\n", + "pares=[]\n", + "impares=[]\n", + "def imp_ou_par(lst):\n", + " for i in lst:\n", + " if i%2==0:\n", + " pares.append(i)\n", + " elif i%2!=0:\n", + " impares.append(i)\n", + " tupla=(len(pares),len(impares))\n", + " print(f'A quantidade de números pares é: {len(pares)}', f'e a quantidade de números ímpares é: {len(impares)}')\n", + " print(f'Logo, a tupla é: {tupla}')\n", + "imp_ou_par(lista_aleatoria)" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "D5A-HqQIopuF", + "outputId": "e772a098-e827-48b3-8580-d7f48bac9007" + }, + "execution_count": 49, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "A quantidade de números pares é: 3 e a quantidade de números ímpares é: 4\n", + "Logo, a tupla é: (3, 4)\n" + ] + } + ] + } + ] +} \ No newline at end of file From cda3eadf450971fccf0c856c29fc365858f86770 Mon Sep 17 00:00:00 2001 From: Marcilio Duarte <104692475+marcilioduarte@users.noreply.github.com> Date: Sat, 9 Jul 2022 18:10:58 -0300 Subject: [PATCH 11/18] =?UTF-8?q?Criado=20atrav=C3=A9s=20do=20Colaboratory?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- ...ece_Aluno_Marcilio_Duarte_Turma_DS38.ipynb | 92 +++++++++++++++++++ 1 file changed, 92 insertions(+) create mode 100644 Desafio_9_de_Python_Awari_Data_Sciece_Aluno_Marcilio_Duarte_Turma_DS38.ipynb diff --git a/Desafio_9_de_Python_Awari_Data_Sciece_Aluno_Marcilio_Duarte_Turma_DS38.ipynb b/Desafio_9_de_Python_Awari_Data_Sciece_Aluno_Marcilio_Duarte_Turma_DS38.ipynb new file mode 100644 index 0000000..589f47f --- /dev/null +++ b/Desafio_9_de_Python_Awari_Data_Sciece_Aluno_Marcilio_Duarte_Turma_DS38.ipynb @@ -0,0 +1,92 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "view-in-github", + "colab_type": "text" + }, + "source": [ + "\"Open" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "49Sr3EsN6B9a" + }, + "source": [ + "![](https://i.imgur.com/YX6UATs.png)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "c1J5PNGv6GK_" + }, + "source": [ + "### Desafio 9\n", + "\n", + "Escreva uma função que retorne a soma dos múltiplos de 3 e 5 entre 0 e um número limite, que vai ser utilizado como parâmetro. \\\n", + "Por exemplo, se o limite for 20, ele retornará a soma de 3, 5, 6, 9, 10, 12, 15, 18, 20." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "KoZW8Ebu-sOp" + }, + "outputs": [], + "source": [ + "def soma_mt_3e5(x):\n", + " y=[a for a in range(1, x+1) if a%3==0 or a%5==0]\n", + " soma=sum(y)\n", + " return soma" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "qPKCOqEyAhUX", + "outputId": "5585534e-a95f-4e30-cac6-e23d99295347" + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "98" + ] + }, + "metadata": {}, + "execution_count": 21 + } + ], + "source": [ + "soma_mt_3e5(20)" + ] + } + ], + "metadata": { + "colab": { + "name": "Desafio 9 de Python - Awari_Data Sciece - Aluno: Marcilio Duarte_Turma_DS38", + "provenance": [], + "authorship_tag": "ABX9TyO7xNNT7IWz9/+AxLeM8aGw", + "include_colab_link": true + }, + "kernelspec": { + "display_name": "Python 3", + "name": "python3" + }, + "language_info": { + "name": "python" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} \ No newline at end of file From 66e71da46b0f7ef9e24f6fabcf6439c91bca784f Mon Sep 17 00:00:00 2001 From: Marcilio Duarte <104692475+marcilioduarte@users.noreply.github.com> Date: Sat, 9 Jul 2022 18:13:08 -0300 Subject: [PATCH 12/18] =?UTF-8?q?Criado=20atrav=C3=A9s=20do=20Colaboratory?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- ...ece_Aluno_Marcilio_Duarte_Turma_DS38.ipynb | 106 ++++++++++++++++++ 1 file changed, 106 insertions(+) create mode 100644 Desafio_8_de_Python_Awari_Data_Sciece_Aluno_Marcilio_Duarte_Turma_DS38.ipynb diff --git a/Desafio_8_de_Python_Awari_Data_Sciece_Aluno_Marcilio_Duarte_Turma_DS38.ipynb b/Desafio_8_de_Python_Awari_Data_Sciece_Aluno_Marcilio_Duarte_Turma_DS38.ipynb new file mode 100644 index 0000000..78b09f6 --- /dev/null +++ b/Desafio_8_de_Python_Awari_Data_Sciece_Aluno_Marcilio_Duarte_Turma_DS38.ipynb @@ -0,0 +1,106 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "name": "Desafio 8 de Python - Awari_Data Sciece - Aluno: Marcilio Duarte_Turma_DS38", + "provenance": [], + "authorship_tag": "ABX9TyMSSSI36flsE0MkU2NVRDau", + "include_colab_link": true + }, + "kernelspec": { + "name": "python3", + "display_name": "Python 3" + }, + "language_info": { + "name": "python" + } + }, + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "view-in-github", + "colab_type": "text" + }, + "source": [ + "\"Open" + ] + }, + { + "cell_type": "markdown", + "source": [ + "![](https://i.imgur.com/YX6UATs.png)\n" + ], + "metadata": { + "id": "MacXeo6FTB7r" + } + }, + { + "cell_type": "markdown", + "source": [ + "### Desafio 8\n", + "\n", + "Escreva um script Python para encontrar as 10 palavras mais longas em um arquivo de texto.\n", + "O arquivo .txt está localizado na mesma pasta do projeto (**texto.txt**)." + ], + "metadata": { + "id": "mqI_oC0ATGbk" + } + }, + { + "cell_type": "code", + "source": [ + "#colei porque não consegui fazer sozinho\n", + "\n", + "from operator import itemgetter\n", + "with open('texto.txt','r') as t_base:\n", + " dic={}\n", + " conteudo=t_base.read().replace('.',' ').replace('-', \" \").replace(',',' ').replace(')', ' ')\n", + " for palavra in conteudo.split():\n", + " dic[f'{palavra}']=len(palavra)\n", + " ordem=sorted(dic.items(),key=itemgetter(1),reverse=True)\n", + " for k, v in enumerate(ordem):\n", + " if k < 10:\n", + " print(v[0],v[1])" + ], + "metadata": { + "id": "IX-Z5n1ATKGR", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "8bdc06bf-6559-4606-ed36-1c6ea0dc65c4" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "comprehensive 13\n", + "intermediate 12\n", + "interpreted 11\n", + "programming 11\n", + "readability 11\n", + "programmers 11\n", + "philosophy 10\n", + "emphasizes 10\n", + "imperative 10\n", + "functional 10\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "" + ], + "metadata": { + "id": "ouy8i6u24W3U" + }, + "execution_count": null, + "outputs": [] + } + ] +} \ No newline at end of file From 7e3f6de6f3687af1d9e654407d6f69c45b41331a Mon Sep 17 00:00:00 2001 From: Marcilio Duarte <104692475+marcilioduarte@users.noreply.github.com> Date: Sun, 17 Jul 2022 17:18:57 -0300 Subject: [PATCH 13/18] =?UTF-8?q?Criado=20atrav=C3=A9s=20do=20Colaboratory?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- ...ece_Aluno_Marcilio_Duarte_Turma_DS38.ipynb | 217 ++++++++++++++++++ 1 file changed, 217 insertions(+) create mode 100644 Desafio_12_de_Python_Awari_Data_Sciece_Aluno_Marcilio_Duarte_Turma_DS38.ipynb diff --git a/Desafio_12_de_Python_Awari_Data_Sciece_Aluno_Marcilio_Duarte_Turma_DS38.ipynb b/Desafio_12_de_Python_Awari_Data_Sciece_Aluno_Marcilio_Duarte_Turma_DS38.ipynb new file mode 100644 index 0000000..94919ee --- /dev/null +++ b/Desafio_12_de_Python_Awari_Data_Sciece_Aluno_Marcilio_Duarte_Turma_DS38.ipynb @@ -0,0 +1,217 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "name": "Desafio 12 de Python - Awari_Data Sciece - Aluno: Marcilio Duarte_Turma_DS38", + "provenance": [], + "authorship_tag": "ABX9TyN1B3CeubEslwNZiHhd77Fo", + "include_colab_link": true + }, + "kernelspec": { + "name": "python3", + "display_name": "Python 3" + }, + "language_info": { + "name": "python" + } + }, + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "view-in-github", + "colab_type": "text" + }, + "source": [ + "\"Open" + ] + }, + { + "cell_type": "markdown", + "source": [ + "![](https://i.imgur.com/YX6UATs.png)" + ], + "metadata": { + "id": "Khf4Hipaom-h" + } + }, + { + "cell_type": "markdown", + "source": [ + "### Desafio 12\n", + "\n", + "Escreva uma função em Python para verificar a validade de uma senha.\n", + "\n", + "A senha deve ter:\n", + "\n", + "* Pelo menos 1 letra entre [a-z] e 1 letra entre [A-Z].\n", + "* Pelo menos 1 número entre [0-9].\n", + "* Pelo menos 1 caractere de [$ # @].\n", + "* Comprimento mínimo de 6 caracteres.\n", + "* Comprimento máximo de 16 caracteres.\n", + "\n", + "Entradas: \"12345678\", \"J3sus0\", \"#Te5t300\", \"J*90j12374\", \"Michheeul\", \"Monk3y6\"\n", + "\n", + "A saída deve ser a senha e um texto indicando se a senha é válida ou inválida:\n", + "\n", + "```\n", + "\"1234\" - Senha inválida\n", + "\"Qw#1234\" - Senha válida\n", + "```" + ], + "metadata": { + "id": "_NGs9dWWodso" + } + }, + { + "cell_type": "code", + "source": [ + "#Resposta 1: Awari" + ], + "metadata": { + "id": "ynQu_IqGyU3k" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "lista_senhas = ['12345678',\n", + " 'J3sus0',\n", + " '#Te5t300',\n", + " 'J*90j12374',\n", + " 'Michheeul',\n", + " 'Monk3y6',\n", + " '1234',\n", + " 'Qw#1234']" + ], + "metadata": { + "id": "Ih84NRo0aGxE" + }, + "execution_count": 86, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "import re\n", + "def valida_senhas(password):\n", + " regex = re.compile(r'^(?=.*[a-z])(?=.*[A-Z])(?=.*[0-9])(?=.*[$#@]).{6,16}$',\n", + " flags=re.M)\n", + " resultado = re.findall(regex, password)\n", + " if resultado:\n", + " print(f'\"{password}\" - Senha válida')\n", + " else:\n", + " print(f'\"{password}\" - Senha inválida')\n" + ], + "metadata": { + "id": "nJtDo945Z4jr" + }, + "execution_count": 87, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "for senha in lista_senhas:\n", + " valida_senhas(senha)" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "mYfAf4EtZ9Ik", + "outputId": "9571d711-bb4c-4778-a59c-0e7279e2c9ed" + }, + "execution_count": 88, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "\"12345678\" - Senha inválida\n", + "\"J3sus0\" - Senha inválida\n", + "\"#Te5t300\" - Senha válida\n", + "\"J*90j12374\" - Senha inválida\n", + "\"Michheeul\" - Senha inválida\n", + "\"Monk3y6\" - Senha inválida\n", + "\"1234\" - Senha inválida\n", + "\"Qw#1234\" - Senha válida\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "### Resposta 2: ERRADA, CORRIGIR COM MENTOR" + ], + "metadata": { + "id": "t5rIQy7VPx63" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "import re\n", + "senha=[\"12345678\", \"J3sus0\", \"#Te5t300\", \"J*90j12374\", \"Michheeul\", \"Monk3y6\"]\n", + "def validacao_senha(n):\n", + " min, mai, num, ces = 0, 0, 0, 0\n", + " if len(n)>=6 & len(n) <=16:\n", + " for i in n:\n", + " if i.islower:\n", + " min+=1\n", + " if i.isupper:\n", + " mai+=1\n", + " if i.isdigit:\n", + " num+=1\n", + " if re.match(r'^(?=.+[$#@])$',n): #####CORRIGIR PARTE DO REGEX\n", + " ces+=1\n", + " if (min>=1 & mai>=1 & num>=1 & ces>=1):\n", + " print(f'{n} - Senha válida') \n", + " else:\n", + " print(f'{n} - Senha inválida') \n", + " else:\n", + " print(f'{n} - Senha inválida')" + ], + "metadata": { + "id": "E18f1DpSr_2w" + }, + "execution_count": 84, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + " for s in senha:\n", + " validacao_senha(s)" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "11-qFWj1Y40s", + "outputId": "641f0881-73b6-462c-b0c8-abc964813452" + }, + "execution_count": 85, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "12345678 - Senha inválida\n", + "J3sus0 - Senha inválida\n", + "#Te5t300 - Senha inválida\n", + "J*90j12374 - Senha inválida\n", + "Michheeul - Senha inválida\n", + "Monk3y6 - Senha inválida\n" + ] + } + ] + } + ] +} \ No newline at end of file From 93d240ec2c659165db5e8171cf7d67bd86535072 Mon Sep 17 00:00:00 2001 From: Marcilio Duarte <104692475+marcilioduarte@users.noreply.github.com> Date: Fri, 19 Aug 2022 23:55:52 -0300 Subject: [PATCH 14/18] =?UTF-8?q?Criado=20atrav=C3=A9s=20do=20Colaboratory?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- Houses_Regression_(Working_Project).ipynb | 5278 +++++++++++++++++++++ 1 file changed, 5278 insertions(+) create mode 100644 Houses_Regression_(Working_Project).ipynb diff --git a/Houses_Regression_(Working_Project).ipynb b/Houses_Regression_(Working_Project).ipynb new file mode 100644 index 0000000..0afe0fb --- /dev/null +++ b/Houses_Regression_(Working_Project).ipynb @@ -0,0 +1,5278 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "name": "Houses_Regression (Working Project)", + "provenance": [], + "authorship_tag": "ABX9TyOXXte6BvmfTOq5KknuTzXV", + "include_colab_link": true + }, + "kernelspec": { + "name": "python3", + "display_name": "Python 3" + }, + "language_info": { + "name": "python" + } + }, + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "view-in-github", + "colab_type": "text" + }, + "source": [ + "\"Open" + ] + }, + { + "cell_type": "markdown", + "source": [ + "##**Como utilizar um modelo de regressão linear para preencher dados nulos do preço de venda de imóveis:**" + ], + "metadata": { + "id": "mcr777QvC6jl" + } + }, + { + "cell_type": "markdown", + "source": [ + "###**Introdução**\n" + ], + "metadata": { + "id": "OTe1lYfqECgZ" + } + }, + { + "cell_type": "markdown", + "source": [ + "Esse projeto trata-se de um exercício de regressão linear da minha especialização na Awari que utilizei como base para construção de portfólio. \n", + "\n", + "Fiz a parte do tratamento dos dados em conjunto com meus colegas de turma @Érika Rocha e @Lucas Castro, mas a partir da etapa de definição das variáveis do modelo de regressão optamos por fazer sozinhos para treinar nossas habilidades. \n", + "\n", + "Os dados nos foram disponibilizados pelo nosso professor, @Anderson Cordeiro, e o objetivo principal do projeto é usar um modelo de regressão linear para estimar e substituir valores nulos do preço de venda dos imóveis da base que não tenham esta informação. " + ], + "metadata": { + "id": "HtczVDg1_4Xf" + } + }, + { + "cell_type": "markdown", + "source": [ + "###***Importando as bibliotecas e unindo os dados***" + ], + "metadata": { + "id": "HQglJjqJxUkE" + } + }, + { + "cell_type": "markdown", + "source": [ + "**Importando as bibliotecas que possivelmente iremos utilizar**" + ], + "metadata": { + "id": "wN1M_ZI5pcAH" + } + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "id": "p9umx0-TwfZ-" + }, + "outputs": [], + "source": [ + "import math\n", + "import statistics\n", + "import numpy as np\n", + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "from sklearn.linear_model import LinearRegression\n", + "from sklearn.model_selection import train_test_split\n", + "from sklearn.metrics import r2_score, mean_absolute_error, mean_squared_error\n", + "import pickle" + ] + }, + { + "cell_type": "code", + "source": [ + "df_test=pd.read_csv('test.csv')" + ], + "metadata": { + "id": "Gs-UED_jAKgt" + }, + "execution_count": 2, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "df_test.info()" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "fDWU-3dLARne", + "outputId": "25f469ea-71fd-4a17-b470-dd2f3ce6442a" + }, + "execution_count": 3, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "\n", + "RangeIndex: 1459 entries, 0 to 1458\n", + "Data columns (total 80 columns):\n", + " # 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..................................................................
14542915160RM21.01936PaveNaNRegLvlAllPub...00NaNNaNNaN062006WDNormal
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\n", + " " + ] + }, + "metadata": {}, + "execution_count": 8 + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "**Juntando as duas tabelas para fazer os demais tratamentos de uma vez só**" + ], + "metadata": { + "id": "9_KnqVD8sTTQ" + } + }, + { + "cell_type": "code", + "source": [ + "df=pd.concat([df_test,df_train],axis=0)\n", + "df.shape" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "W8NIWs8V0042", + "outputId": "bbdf237b-7ef5-451c-c043-85ddbdb1666c" + }, + "execution_count": 9, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "(2919, 82)" + ] + }, + "metadata": {}, + "execution_count": 9 + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "### ***Análise Exploratória e Tratamentos Iniciais***" + ], + "metadata": { + "id": "KXFsKysVtMt6" + } + }, + { + "cell_type": "markdown", + "source": [ + "Tarefas que podem ser executadas nesta base:\n", + "\n", + "\n", + "1. Tratar os nulos e os valores de NAs que estão como nulos, mas que se tratam de categorias em variáveis categóricas.\n", + "2. Verificar se tem alguma variável com valores constantes com o .describe()\n", + "2. Verificar se tem alguma variável com categorias tipo que não correspondem com o esperado com o value_counts ou com o unique;\n", + "3. Criar as dummies ou categorias numéricas que forem necessárias em variáveis categóricas;" + ], + "metadata": { + "id": "nW_3QYQ4tZjX" + } + }, + { + "cell_type": "code", + "source": [ + "df.info()" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "g83LvL7Q1rBr", + "outputId": "cdeeaaa1-5a22-4cf0-8ee1-a77aa3b0bad3" + }, + "execution_count": 10, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "\n", + "Int64Index: 2919 entries, 0 to 1459\n", + "Data columns (total 82 columns):\n", + " # Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 Id 2919 non-null int64 \n", + " 1 MSSubClass 2919 non-null int64 \n", + " 2 MSZoning 2915 non-null object \n", + " 3 LotFrontage 2433 non-null float64\n", + " 4 LotArea 2919 non-null int64 \n", + " 5 Street 2919 non-null object \n", + " 6 Alley 198 non-null object \n", + " 7 LotShape 2919 non-null object \n", + " 8 LandContour 2919 non-null object \n", + " 9 Utilities 2917 non-null object \n", + " 10 LotConfig 2919 non-null object \n", + " 11 LandSlope 2919 non-null object \n", + " 12 Neighborhood 2919 non-null object \n", + " 13 Condition1 2919 non-null object \n", + " 14 Condition2 2919 non-null object \n", + " 15 BldgType 2919 non-null object \n", + " 16 HouseStyle 2919 non-null object \n", + " 17 OverallQual 2919 non-null int64 \n", + " 18 OverallCond 2919 non-null int64 \n", + " 19 YearBuilt 2919 non-null int64 \n", + " 20 YearRemodAdd 2919 non-null int64 \n", + " 21 RoofStyle 2919 non-null object \n", + " 22 RoofMatl 2919 non-null object \n", + " 23 Exterior1st 2918 non-null object \n", + " 24 Exterior2nd 2918 non-null object \n", + " 25 MasVnrType 2895 non-null object \n", + " 26 MasVnrArea 2896 non-null float64\n", + " 27 ExterQual 2919 non-null object \n", + " 28 ExterCond 2919 non-null object \n", + " 29 Foundation 2919 non-null object \n", + " 30 BsmtQual 2838 non-null object \n", + " 31 BsmtCond 2837 non-null object \n", + " 32 BsmtExposure 2837 non-null object \n", + " 33 BsmtFinType1 2840 non-null object \n", + " 34 BsmtFinSF1 2918 non-null float64\n", + " 35 BsmtFinType2 2839 non-null object \n", + " 36 BsmtFinSF2 2918 non-null float64\n", + " 37 BsmtUnfSF 2918 non-null float64\n", + " 38 TotalBsmtSF 2918 non-null float64\n", + " 39 Heating 2919 non-null object \n", + " 40 HeatingQC 2919 non-null object \n", + " 41 CentralAir 2919 non-null object \n", + " 42 Electrical 2918 non-null object \n", + " 43 1stFlrSF 2919 non-null int64 \n", + " 44 2ndFlrSF 2919 non-null int64 \n", + " 45 LowQualFinSF 2919 non-null int64 \n", + " 46 GrLivArea 2919 non-null int64 \n", + " 47 BsmtFullBath 2917 non-null float64\n", + " 48 BsmtHalfBath 2917 non-null float64\n", + " 49 FullBath 2919 non-null int64 \n", + " 50 HalfBath 2919 non-null int64 \n", + " 51 BedroomAbvGr 2919 non-null int64 \n", + " 52 KitchenAbvGr 2919 non-null int64 \n", + " 53 KitchenQual 2918 non-null object \n", + " 54 TotRmsAbvGrd 2919 non-null int64 \n", + " 55 Functional 2917 non-null object \n", + " 56 Fireplaces 2919 non-null int64 \n", + " 57 FireplaceQu 1499 non-null object \n", + " 58 GarageType 2762 non-null object \n", + " 59 GarageYrBlt 2760 non-null float64\n", + " 60 GarageFinish 2760 non-null object \n", + " 61 GarageCars 2918 non-null float64\n", + " 62 GarageArea 2918 non-null float64\n", + " 63 GarageQual 2760 non-null object \n", + " 64 GarageCond 2760 non-null object \n", + " 65 PavedDrive 2919 non-null object \n", + " 66 WoodDeckSF 2919 non-null int64 \n", + " 67 OpenPorchSF 2919 non-null int64 \n", + " 68 EnclosedPorch 2919 non-null int64 \n", + " 69 3SsnPorch 2919 non-null int64 \n", + " 70 ScreenPorch 2919 non-null int64 \n", + " 71 PoolArea 2919 non-null int64 \n", + " 72 PoolQC 10 non-null object \n", + " 73 Fence 571 non-null object \n", + " 74 MiscFeature 105 non-null object \n", + " 75 MiscVal 2919 non-null int64 \n", + " 76 MoSold 2919 non-null int64 \n", + " 77 YrSold 2919 non-null int64 \n", + " 78 SaleType 2918 non-null object \n", + " 79 SaleCondition 2919 non-null object \n", + " 80 istrain 2919 non-null int64 \n", + " 81 SalePrice 1460 non-null float64\n", + "dtypes: float64(12), int64(27), object(43)\n", + "memory usage: 1.8+ MB\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "df.columns" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "4T3zsguQKndv", + "outputId": "2493e260-912d-49fe-8e90-278b00a720ca" + }, + "execution_count": 11, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "Index(['Id', 'MSSubClass', 'MSZoning', 'LotFrontage', 'LotArea', 'Street',\n", + " 'Alley', 'LotShape', 'LandContour', 'Utilities', 'LotConfig',\n", + " 'LandSlope', 'Neighborhood', 'Condition1', 'Condition2', 'BldgType',\n", + " 'HouseStyle', 'OverallQual', 'OverallCond', 'YearBuilt', 'YearRemodAdd',\n", + " 'RoofStyle', 'RoofMatl', 'Exterior1st', 'Exterior2nd', 'MasVnrType',\n", + " 'MasVnrArea', 'ExterQual', 'ExterCond', 'Foundation', 'BsmtQual',\n", + " 'BsmtCond', 'BsmtExposure', 'BsmtFinType1', 'BsmtFinSF1',\n", + " 'BsmtFinType2', 'BsmtFinSF2', 'BsmtUnfSF', 'TotalBsmtSF', 'Heating',\n", + " 'HeatingQC', 'CentralAir', 'Electrical', '1stFlrSF', '2ndFlrSF',\n", + " 'LowQualFinSF', 'GrLivArea', 'BsmtFullBath', 'BsmtHalfBath', 'FullBath',\n", + " 'HalfBath', 'BedroomAbvGr', 'KitchenAbvGr', 'KitchenQual',\n", + " 'TotRmsAbvGrd', 'Functional', 'Fireplaces', 'FireplaceQu', 'GarageType',\n", + " 'GarageYrBlt', 'GarageFinish', 'GarageCars', 'GarageArea', 'GarageQual',\n", + " 'GarageCond', 'PavedDrive', 'WoodDeckSF', 'OpenPorchSF',\n", + " 'EnclosedPorch', '3SsnPorch', 'ScreenPorch', 'PoolArea', 'PoolQC',\n", + " 'Fence', 'MiscFeature', 'MiscVal', 'MoSold', 'YrSold', 'SaleType',\n", + " 'SaleCondition', 'istrain', 'SalePrice'],\n", + " dtype='object')" + ] + }, + "metadata": {}, + "execution_count": 11 + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "#### **Tratando os nulos das categorias em que NA é uma categoria.**" + ], + "metadata": { + "id": "lZn1irwboUO4" + } + }, + { + "cell_type": "code", + "source": [ + "df['Alley'].fillna(\"no_access\",inplace=True)\n", + "df['BsmtQual'].fillna('no_bsmt',inplace=True)\n", + "df['BsmtCond'].fillna('no_bsmt',inplace=True)\n", + "df['BsmtExposure'].fillna('no_bsmt',inplace=True)\n", + "df['BsmtFinType1'].fillna('no_bsmt',inplace=True)\n", + "df['BsmtFinType2'].fillna('no_bsmt',inplace=True)\n", + "df['Fireplaces'].fillna('no_fireplc',inplace=True)\n", + "df['GarageType'].fillna('no_garage',inplace=True)\n", + "df['GarageFinish'].fillna('no_garage',inplace=True)\n", + "df['GarageQual'].fillna('no_garage',inplace=True)\n", + "df['GarageCond'].fillna('no_garage',inplace=True)\n", + "df['PoolQC'].fillna('no_pool',inplace=True)\n", + "df['Fence'].fillna(\"no_fence\",inplace=True)\n", + "df['MiscFeature'].fillna(\"none\",inplace=True)\n" + ], + "metadata": { + "id": "DrCsB0f0Gzj_" + }, + "execution_count": 12, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "df['Alley'].value_counts()" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "iLQg4p_-2xyY", + "outputId": "334ff79e-af64-43bc-8211-7cebcd358cb2" + }, + "execution_count": 13, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "no_access 2721\n", + "Grvl 120\n", + "Pave 78\n", + "Name: Alley, dtype: int64" + ] + }, + "metadata": {}, + "execution_count": 13 + } + ] + }, + { + "cell_type": "code", + "source": [ + "##COLUNAS MARCILIO\n", + "\n", + "df[['Id', 'MSSubClass', 'MSZoning', 'LotFrontage', 'LotArea', 'Street',\n", + " 'Alley', 'LotShape', 'LandContour', 'Utilities', 'LotConfig',\n", + " 'LandSlope', 'Neighborhood', 'Condition1', 'Condition2', 'BldgType',\n", + " 'HouseStyle', 'OverallQual', 'OverallCond', 'YearBuilt', 'YearRemodAdd',\n", + " 'RoofStyle', 'RoofMatl', 'Exterior1st', 'Exterior2nd', 'MasVnrType',\n", + " 'MasVnrArea', 'ExterQual']].describe().round(2)" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 0 + }, + "id": "yOngNXx5L8i5", + "outputId": "afe08f46-5720-43fe-e23b-af8fd3074b28" + }, + "execution_count": 14, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + " Id MSSubClass LotFrontage LotArea OverallQual OverallCond \\\n", + "count 2919.00 2919.00 2433.00 2919.00 2919.00 2919.00 \n", + "mean 1460.00 57.14 69.31 10168.11 6.09 5.56 \n", + "std 842.79 42.52 23.34 7887.00 1.41 1.11 \n", + "min 1.00 20.00 21.00 1300.00 1.00 1.00 \n", + "25% 730.50 20.00 59.00 7478.00 5.00 5.00 \n", + "50% 1460.00 50.00 68.00 9453.00 6.00 5.00 \n", + "75% 2189.50 70.00 80.00 11570.00 7.00 6.00 \n", + "max 2919.00 190.00 313.00 215245.00 10.00 9.00 \n", + "\n", + " YearBuilt YearRemodAdd MasVnrArea \n", + "count 2919.00 2919.00 2896.00 \n", + "mean 1971.31 1984.26 102.20 \n", + "std 30.29 20.89 179.33 \n", + "min 1872.00 1950.00 0.00 \n", + "25% 1953.50 1965.00 0.00 \n", + "50% 1973.00 1993.00 0.00 \n", + "75% 2001.00 2004.00 164.00 \n", + "max 2010.00 2010.00 1600.00 " + ], + "text/html": [ + "\n", + "
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IdMSSubClassLotFrontageLotAreaOverallQualOverallCondYearBuiltYearRemodAddMasVnrArea
count2919.002919.002433.002919.002919.002919.002919.002919.002896.00
mean1460.0057.1469.3110168.116.095.561971.311984.26102.20
std842.7942.5223.347887.001.411.1130.2920.89179.33
min1.0020.0021.001300.001.001.001872.001950.000.00
25%730.5020.0059.007478.005.005.001953.501965.000.00
50%1460.0050.0068.009453.006.005.001973.001993.000.00
75%2189.5070.0080.0011570.007.006.002001.002004.00164.00
max2919.00190.00313.00215245.0010.009.002010.002010.001600.00
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\n", + " " + ] + }, + "metadata": {}, + "execution_count": 14 + } + ] + }, + { + "cell_type": "code", + "source": [ + "## Nenhuma delas tem constante porque o desvio padrão não foi igual a 0 em nenhum caso." + ], + "metadata": { + "id": "mmW-cFqHvZkt" + }, + "execution_count": 15, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "####**Outro exemplo de tratamento que poderia ser aplicado**" + ], + "metadata": { + "id": "LFrdi2q1pEXW" + } + }, + { + "cell_type": "markdown", + "source": [ + "Como nosso objetivo principal neste exercício não é treinar a habilidade de tratar os dados (porque já fizemos isso em outros exercícios), fomos orientados a não perder muito tempo fazendo todos os tratamentos possíveis. \n", + "\n", + "Porém, abaixo há um modelo de tratamento que poderia ser adotado para todas as variáveis categóricas." + ], + "metadata": { + "id": "dKHUTe58E5dO" + } + }, + { + "cell_type": "code", + "source": [ + "ls_sub= {'LotShape': {'Reg':4, 'IR1':3, 'IR2':2, 'IR3':1}}\n", + "df_2=df.copy()\n", + "df_2=df.replace(ls_sub)\n", + "df_2[['LotShape']]" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 0 + }, + "id": "vGeWyPG-6y3i", + "outputId": "da8cf0a3-136b-49d7-a212-686b2c30a297" + }, + "execution_count": 16, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + " LotShape\n", + "0 4\n", + "1 3\n", + "2 3\n", + "3 3\n", + "4 3\n", + "... ...\n", + "1455 4\n", + "1456 4\n", + "1457 4\n", + "1458 4\n", + "1459 4\n", + "\n", + "[2919 rows x 1 columns]" + ], + "text/html": [ + "\n", + "
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2919 rows × 1 columns

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\n", + " " + ] + }, + "metadata": {}, + "execution_count": 16 + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "### ***Análise de Correlação para Regressão***" + ], + "metadata": { + "id": "XOESx5YXICzj" + } + }, + { + "cell_type": "markdown", + "source": [ + "***Análise da correlação entre as variáveis que eu fiquei responsável e a variável dependente (saleprice)***" + ], + "metadata": { + "id": "bswLkMHIfjDa" + } + }, + { + "cell_type": "code", + "source": [ + "cm1=df[['MSSubClass', 'MSZoning', 'LotFrontage', 'LotArea', 'Street',\n", + " 'Alley', 'LotShape', 'LandContour', 'Utilities', 'LotConfig',\n", + " 'LandSlope', 'Neighborhood', 'Condition1', 'Condition2', 'BldgType',\n", + " 'HouseStyle', 'OverallQual', 'OverallCond', 'YearBuilt', 'YearRemodAdd',\n", + " 'RoofStyle', 'RoofMatl', 'Exterior1st', 'Exterior2nd', 'MasVnrType',\n", + " 'MasVnrArea', 'ExterQual', 'SalePrice']].corr().round(2)" + ], + "metadata": { + "id": "QBai1I1-46kL" + }, + "execution_count": 17, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "fig, ax = plt.subplots(figsize=(24,16))\n", + "sns.heatmap(cm1, ax=ax, vmin=-1.0,vmax=1.0, annot=True,cmap='RdYlGn') \n", + "plt.show()" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 0 + }, + "id": "RPOUg5rZ_UZM", + "outputId": "338b3db8-3814-4557-96d4-be066f255888" + }, + "execution_count": 18, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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\n" + }, + "metadata": { + "needs_background": "light" + } + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "**Análise da correlação unindo as minhas com as variáveis dos colegas**" + ], + "metadata": { + "id": "udPMaPm6rSny" + } + }, + { + "cell_type": "code", + "source": [ + "cm2=df[[ 'SalePrice','MasVnrArea', 'LotFrontage', 'GarageYrBlt','OverallQual', 'YearBuilt', 'YearRemodAdd', 'Fireplaces', \n", + " 'GarageCars', 'GarageArea', 'BsmtFinSF1', 'TotalBsmtSF',\n", + " '1stFlrSF','GrLivArea', 'FullBath']].corr().round(2)" + ], + "metadata": { + "id": "_ko52gkuMkjB" + }, + "execution_count": 19, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "fig, ax = plt.subplots(figsize=(24,16))\n", + "sns.heatmap(cm2, ax=ax, vmin=-1.0,vmax=1.0, annot=True,cmap='RdYlGn') \n", + "plt.show()" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 0 + }, + "id": "7IrogIZ3NFH0", + "outputId": "df58f3a4-eaf9-4db2-cf83-3675bbe8f59f" + }, + "execution_count": 20, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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+ }, + "metadata": { + "needs_background": "light" + } + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "### **Estimando o 'SalePrice' com base nas variáveis definidas pela análise de correlação**" + ], + "metadata": { + "id": "96l1c5czsVbE" + } + }, + { + "cell_type": "markdown", + "source": [ + "####**Aplicando a regressão na tabela de treino**" + ], + "metadata": { + "id": "z_xoJmY7-nM7" + } + }, + { + "cell_type": "markdown", + "source": [ + "Primeiro, vamos separar os dados de novo" + ], + "metadata": { + "id": "5hewDznG40ue" + } + }, + { + "cell_type": "code", + "source": [ + "df_test_2=df[df['istrain']==0]" + ], + "metadata": { + "id": "opGvH4cHvh7L" + }, + "execution_count": 21, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "df_train_2=df[df['istrain']==1]" + ], + "metadata": { + "id": "ozjhtinEvzJb" + }, + "execution_count": 22, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "df_train_2.head()" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 0 + }, + "id": "g55RGTAFvvt1", + "outputId": "4b13ee8d-323c-439f-bd9c-e5b701c37af9" + }, + "execution_count": 23, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + " Id MSSubClass MSZoning LotFrontage LotArea Street Alley LotShape \\\n", + "0 1 60 RL 65.0 8450 Pave no_access Reg \n", + "1 2 20 RL 80.0 9600 Pave no_access Reg \n", + "2 3 60 RL 68.0 11250 Pave no_access IR1 \n", + "3 4 70 RL 60.0 9550 Pave no_access IR1 \n", + "4 5 60 RL 84.0 14260 Pave no_access IR1 \n", + "\n", + " LandContour Utilities ... PoolQC Fence MiscFeature MiscVal MoSold \\\n", + "0 Lvl AllPub ... no_pool no_fence none 0 2 \n", + "1 Lvl AllPub ... no_pool no_fence none 0 5 \n", + "2 Lvl AllPub ... no_pool no_fence none 0 9 \n", + "3 Lvl AllPub ... no_pool no_fence none 0 2 \n", + "4 Lvl AllPub ... no_pool no_fence none 0 12 \n", + "\n", + " YrSold SaleType SaleCondition istrain SalePrice \n", + "0 2008 WD Normal 1 208500.0 \n", + "1 2007 WD Normal 1 181500.0 \n", + "2 2008 WD Normal 1 223500.0 \n", + "3 2006 WD Abnorml 1 140000.0 \n", + "4 2008 WD Normal 1 250000.0 \n", + "\n", + "[5 rows x 82 columns]" + ], + "text/html": [ + "\n", + "
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IdMSSubClassMSZoningLotFrontageLotAreaStreetAlleyLotShapeLandContourUtilities...PoolQCFenceMiscFeatureMiscValMoSoldYrSoldSaleTypeSaleConditionistrainSalePrice
0160RL65.08450Paveno_accessRegLvlAllPub...no_poolno_fencenone022008WDNormal1208500.0
1220RL80.09600Paveno_accessRegLvlAllPub...no_poolno_fencenone052007WDNormal1181500.0
2360RL68.011250Paveno_accessIR1LvlAllPub...no_poolno_fencenone092008WDNormal1223500.0
3470RL60.09550Paveno_accessIR1LvlAllPub...no_poolno_fencenone022006WDAbnorml1140000.0
4560RL84.014260Paveno_accessIR1LvlAllPub...no_poolno_fencenone0122008WDNormal1250000.0
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\n", + " " + ] + }, + "metadata": {}, + "execution_count": 23 + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "As variáveis explicativas escolhidas fazem parte do grupo de variáveis da matriz de correlação 2. Porém, a fim de simplificar o processo, optei por retirar variáveis com valores nulos." + ], + "metadata": { + "id": "fD52O25i1Vdd" + } + }, + { + "cell_type": "code", + "source": [ + "lr=LinearRegression()\n", + "X=df_train_2[['OverallQual', 'YearBuilt', 'YearRemodAdd', 'Fireplaces', \n", + " 'GarageCars', 'GarageArea', 'BsmtFinSF1', 'TotalBsmtSF',\n", + " '1stFlrSF','GrLivArea', 'FullBath']]\n", + "Y=df_train_2[['SalePrice']]" + ], + "metadata": { + "id": "7CsuVLQQwxPn" + }, + "execution_count": 24, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "Vou separar para o teste 30% da amostra e para validação 70%, esse método é chamado de holdout e é bem aceito no meio corporativo. 70% da amostra irá passar pelo FIT e os outros 30% serão utilizados para teste. O random state irá fixar ou não um valor para o train, teste e split começar. " + ], + "metadata": { + "id": "J4tRtLZR1IHl" + } + }, + { + "cell_type": "code", + "source": [ + "X_train, X_valid, Y_train, Y_valid = train_test_split(X, Y, random_state=42, test_size=0.3) " + ], + "metadata": { + "id": "omS1QI5EsUCA" + }, + "execution_count": 25, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "Fitando o modelo de regressão linear com as variáveis explicativas e dependente de treino." + ], + "metadata": { + "id": "5ZiDAj6wZ-7C" + } + }, + { + "cell_type": "code", + "source": [ + "lr.fit(X_train,Y_train)" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "CVl4KDZR1ENk", + "outputId": "b0c5841c-4d93-432f-bfa8-cee21df7795b" + }, + "execution_count": 26, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "LinearRegression()" + ] + }, + "metadata": {}, + "execution_count": 26 + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "Criando uma matriz de estimativas do SalePrice com base na matriz das variáveis explicativas de validação." + ], + "metadata": { + "id": "-r9eGJhFaL16" + } + }, + { + "cell_type": "code", + "source": [ + "Yhat=lr.predict(X_valid)" + ], + "metadata": { + "id": "CXn6-84c17xB" + }, + "execution_count": 27, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "Yhat" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "F5n7VcxQ3DZ7", + "outputId": "0f050969-d52b-4de7-deb8-a2d7fb99b8c8" + }, + "execution_count": 28, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "array([[149329.20817408],\n", + " [304507.82109333],\n", + " [113937.26869998],\n", + " [179644.60841616],\n", + " [297971.06848695],\n", + " [ 51654.09390535],\n", + " [229800.17596515],\n", + " [174576.18538435],\n", + " [ 50145.72204022],\n", + " [109802.73504659],\n", + " [151691.88822251],\n", + " [105512.70899166],\n", + " [ 80119.50197208],\n", + " [206704.95937333],\n", + " 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\n", + " " + ] + }, + "metadata": {}, + "execution_count": 29 + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "Importando as métricas e verificando a explicabilidade do meu modelo." + ], + "metadata": { + "id": "0wYP9glCahj3" + } + }, + { + "cell_type": "code", + "source": [ + "r2= r2_score(Y_valid,Yhat)\n", + "print('As variáveis explicativas do modelo explicam as variações no preço de venda dos imóveis em:',(r2*100).round(2),'%')" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "9RKNUnC_3ky3", + "outputId": "dab95c1e-e22c-4d5f-d495-70fb9ba743fa" + }, + "execution_count": 30, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "As variáveis explicativas do modelo explicam as variações no preço de venda dos imóveis em: 80.83 %\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "m_abe=mean_absolute_error(Y_valid,Yhat)\n", + "print('O erro médio absoluto do modelo é:', (m_abe).round(2))" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "bM8USBis32KO", + "outputId": "5b6add35-3199-45e5-97a5-ed026cb83b48" + }, + "execution_count": 31, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "O erro médio absoluto do modelo é: 24232.33\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "m_sqe=mean_squared_error(Y_valid,Yhat)\n", + "print('O erro médio quadrático do modelo é:', (m_sqe).round(2))" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "rgGd14DT4Yja", + "outputId": "5743e120-38d3-48c1-87af-214d8097ba75" + }, + "execution_count": 32, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "O erro médio quadrático do modelo é: 1337499031.46\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "m_sqe_sqrt=math.sqrt(m_sqe)\n", + "print('A raiz quadrada do erro médio quadrático é:', (m_sqe_sqrt))" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "T5a0AjLLhj6M", + "outputId": "e23a9eed-4511-4dcb-a65b-962c18ca0de1" + }, + "execution_count": 33, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "A raiz quadrada do erro médio quadrático é: 36571.833854163844\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "1. Modelo A:\n", + "\n", + " *R2: 80,83%*\n", + "\n", + " *MAE: 24.232,33 UM*\n", + "\n", + " *MSE: 1.337.499.031,46 UM*" + ], + "metadata": { + "id": "PeBepZAubLI6" + } + }, + { + "cell_type": "code", + "source": [ + "lr.coef_" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "EwjKTRJgdZQg", + "outputId": "7c4b6482-7df7-46ba-a46a-f4fc3517579d" + }, + "execution_count": 34, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "array([[ 1.92568904e+04, 2.27841743e+02, 3.47997538e+02,\n", + " 9.61594675e+03, 1.50860924e+04, 5.37442854e+00,\n", + " 1.87733318e+01, 3.55529028e+00, 1.05877616e+01,\n", + " 4.13827981e+01, -1.58648955e+03]])" + ] + }, + "metadata": {}, + "execution_count": 34 + } + ] + }, + { + "cell_type": "code", + "source": [ + "lr.intercept_" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "QQ3OJWAwdk2V", + "outputId": "16df4e65-5477-4fd8-b77f-8e329840bda4" + }, + "execution_count": 35, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "array([-1197139.32509137])" + ] + }, + "metadata": {}, + "execution_count": 35 + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "Salvando os resultados do melhor modelo com a biblioteca pickle" + ], + "metadata": { + "id": "dq_kYsxeekuO" + } + }, + { + "cell_type": "code", + "source": [ + "with open('LinearRegression.pkl', 'wb') as modelo:\n", + " pickle.dump(lr,modelo) " + ], + "metadata": { + "id": "1g0Bq1steai-" + }, + "execution_count": 36, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "with open('LinearRegression.pkl', 'rb') as modelo:\n", + " regressao=pickle.load(modelo)" + ], + "metadata": { + "id": "hYxn6xcEecqf" + }, + "execution_count": 37, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "regressao" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "vPeLorKgelZ7", + "outputId": "909b3ecb-0220-4b6c-ccb1-6f14b6898566" + }, + "execution_count": 38, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "LinearRegression()" + ] + }, + "metadata": {}, + "execution_count": 38 + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "####**Aplicando a regressão na tabela de teste**" + ], + "metadata": { + "id": "BmMG5poglBDK" + } + }, + { + "cell_type": "markdown", + "source": [ + "Antes de mais nada, vamos verificar se há nulos nas variáveis explicativas para evitar problemas na hora de estimar a regressão" + ], + "metadata": { + "id": "ad9qI2tiqewk" + } + }, + { + "cell_type": "code", + "source": [ + "df_test_2[['OverallQual', 'YearBuilt', 'YearRemodAdd', 'Fireplaces', \n", + " 'GarageCars', 'GarageArea', 'BsmtFinSF1', 'TotalBsmtSF',\n", + " '1stFlrSF','GrLivArea', 'FullBath']].info()" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "2tE6KD7omY8P", + "outputId": "89459642-bc8b-4ada-c1ab-69e088fba9a5" + }, + "execution_count": 39, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "\n", + "Int64Index: 1459 entries, 0 to 1458\n", + "Data columns (total 11 columns):\n", + " # Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 OverallQual 1459 non-null int64 \n", + " 1 YearBuilt 1459 non-null int64 \n", + " 2 YearRemodAdd 1459 non-null int64 \n", + " 3 Fireplaces 1459 non-null int64 \n", + " 4 GarageCars 1458 non-null float64\n", + " 5 GarageArea 1458 non-null float64\n", + " 6 BsmtFinSF1 1458 non-null float64\n", + " 7 TotalBsmtSF 1458 non-null float64\n", + " 8 1stFlrSF 1459 non-null int64 \n", + " 9 GrLivArea 1459 non-null int64 \n", + " 10 FullBath 1459 non-null int64 \n", + "dtypes: float64(4), int64(7)\n", + "memory usage: 136.8 KB\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "As variáveis: \n", + "\n", + "1. 'GarageCars'\n", + "\n", + "2. 'GarageArea'\n", + "\n", + "3. 'BsmtFinSF1'\n", + "\n", + "4. 'TotalBsmtSF'\n", + "\n", + "Apresentam 1 observação com dados nulos.\n", + "\n", + "Vamos substituir esses nulos por" + ], + "metadata": { + "id": "JWHeo_Icqtqx" + } + }, + { + "cell_type": "code", + "source": [ + "df_test_2[['GarageCars','GarageArea', 'BsmtFinSF1','TotalBsmtSF']].mean().round(2)" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "vw32OUMcpjU3", + "outputId": "56fbb400-c84d-4331-ceea-23301d8222d8" + }, + "execution_count": 40, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "GarageCars 1.77\n", + "GarageArea 472.77\n", + "BsmtFinSF1 439.20\n", + "TotalBsmtSF 1046.12\n", + "dtype: float64" + ] + }, + "metadata": {}, + "execution_count": 40 + } + ] + }, + { + "cell_type": "code", + "source": [ + "df_test_2[['GarageCars','GarageArea', 'BsmtFinSF1','TotalBsmtSF']].median().round(2)" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "f-OQ_5njp9um", + "outputId": "797afee7-3d4e-4413-d1b6-0fd83c76c7d1" + }, + "execution_count": 41, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "GarageCars 2.0\n", + "GarageArea 480.0\n", + "BsmtFinSF1 350.5\n", + "TotalBsmtSF 988.0\n", + "dtype: float64" + ] + }, + "metadata": {}, + "execution_count": 41 + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "Para me ajudar a decidir se substituo pela média ou mediana, vou plotar boxplots." + ], + "metadata": { + "id": "_dgrzsqsxsBI" + } + }, + { + "cell_type": "code", + "source": [ + "fig, axes =plt.subplots(2,2,figsize=[10,10])\n", + "\n", + "sns.boxplot(data=df_test_2,x='GarageCars',ax=axes[0,0],palette='pastel').set_title('Nº de Carros na Garagem')\n", + "sns.boxplot(data=df_test_2, x='GarageArea',ax=axes[0,1],palette='dark').set_title('Área da Garagem')\n", + "sns.boxplot(data=df_test_2,x='BsmtFinSF1',ax=axes[1,0],palette='bright').set_title('Ft² de Porão Tipo 1 Finalizado')\n", + "sns.boxplot(data=df_test_2,x='TotalBsmtSF',ax=axes[1,1],palette='deep').set_title('Ft² total do porão')\n", + "\n", + "fig.suptitle('BoxPlots',fontsize=20)\n", + "\n", + "sns.boxplot(data=df_test_2,x=df_test_2['TotalBsmtSF'])\n", + "plt.show()" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 0 + }, + "id": "JmfgcrWyrxXg", + "outputId": "dcfabab8-0178-472e-939d-f8ab8462bc8d" + }, + "execution_count": 42, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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\n" + }, + "metadata": { + "needs_background": "light" + } + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "Ok, agora vamos fazer a substituição utilizando a mediana para diminuir o impacto de outliers nos nossos resultados" + ], + "metadata": { + "id": "93ZeVHUNxzkb" + } + }, + { + "cell_type": "code", + "source": [ + "df_test_2['GarageCars'].fillna(df_test_2['GarageCars'].median(),inplace=True)\n", + "df_test_2['GarageArea'].fillna(df_test_2['GarageArea'].median(),inplace=True)\n", + "df_test_2['BsmtFinSF1'].fillna(df_test_2['BsmtFinSF1'].median(),inplace=True)\n", + "df_test_2['TotalBsmtSF'].fillna(df_test_2['TotalBsmtSF'].median(),inplace=True)" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "8Db7R0zCnDGk", + "outputId": "b98a0912-a5a4-4ed2-b017-1bf6b786e0fb" + }, + "execution_count": 43, + "outputs": [ + { + "output_type": "stream", + "name": "stderr", + "text": [ + "/usr/local/lib/python3.7/dist-packages/pandas/core/generic.py:6392: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " return self._update_inplace(result)\n", + "/usr/local/lib/python3.7/dist-packages/pandas/core/generic.py:6392: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " return self._update_inplace(result)\n", + "/usr/local/lib/python3.7/dist-packages/pandas/core/generic.py:6392: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " return self._update_inplace(result)\n", + "/usr/local/lib/python3.7/dist-packages/pandas/core/generic.py:6392: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " return self._update_inplace(result)\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "Agora vamos verificar se deu certo, e caso funcione vamos estimar o Sale Price para o modelo com as variáveis explicativas da base de teste." + ], + "metadata": { + "id": "p7gRm1MA4mXA" + } + }, + { + "cell_type": "code", + "source": [ + "df_test_2[['OverallQual', 'YearBuilt', 'YearRemodAdd', 'Fireplaces', \n", + " 'GarageCars', 'GarageArea', 'BsmtFinSF1', 'TotalBsmtSF',\n", + " '1stFlrSF','GrLivArea', 'FullBath']].info()" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "RnBuW91fye7B", + "outputId": "55f22f58-c62c-4b2f-bb58-8e2d19a86d32" + }, + "execution_count": 44, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "\n", + "Int64Index: 1459 entries, 0 to 1458\n", + "Data columns (total 11 columns):\n", + " # Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 OverallQual 1459 non-null int64 \n", + " 1 YearBuilt 1459 non-null int64 \n", + " 2 YearRemodAdd 1459 non-null int64 \n", + " 3 Fireplaces 1459 non-null int64 \n", + " 4 GarageCars 1459 non-null float64\n", + " 5 GarageArea 1459 non-null float64\n", + " 6 BsmtFinSF1 1459 non-null float64\n", + " 7 TotalBsmtSF 1459 non-null float64\n", + " 8 1stFlrSF 1459 non-null int64 \n", + " 9 GrLivArea 1459 non-null int64 \n", + " 10 FullBath 1459 non-null int64 \n", + "dtypes: float64(4), int64(7)\n", + "memory usage: 136.8 KB\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "X_2=df_test_2[['OverallQual', 'YearBuilt', 'YearRemodAdd', 'Fireplaces', \n", + " 'GarageCars', 'GarageArea', 'BsmtFinSF1', 'TotalBsmtSF',\n", + " '1stFlrSF','GrLivArea', 'FullBath']]" + ], + "metadata": { + "id": "egiGP2zefq52" + }, + "execution_count": 45, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "Yhat_2=regressao.predict(X_2).round(2)\n", + "Yhat_2" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "bl_VsVBOlnni", + "outputId": "81659529-0407-4098-ea78-4f464e84e60c" + }, + "execution_count": 46, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "array([[104276.2 ],\n", + " [152193.39],\n", + " [184036.83],\n", + " ...,\n", + " [172557.54],\n", + " [104610.97],\n", + " [251931.48]])" + ] + }, + "metadata": {}, + "execution_count": 46 + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "Preços estimados, agora vamos incluí-los em uma nova variável na tabela e substituir os nulos da variável antiga pelos valores da variável nova." + ], + "metadata": { + "id": "o3POmRWh7rUl" + } + }, + { + "cell_type": "code", + "source": [ + "df_test_2[['SalePrice_est']]=Yhat_2\n", + "df_test_2.head()" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 0 + }, + "id": "FMP0ep5N6H3P", + "outputId": "2f3a143b-8f86-4b83-d393-7613b323a8ae" + }, + "execution_count": 47, + "outputs": [ + { + "output_type": "stream", + "name": "stderr", + "text": [ + "/usr/local/lib/python3.7/dist-packages/pandas/core/frame.py:3678: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " self[col] = igetitem(value, i)\n" + ] + }, + { + "output_type": "execute_result", + "data": { + "text/plain": [ + " Id MSSubClass MSZoning LotFrontage LotArea Street Alley 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Fence MiscFeature MiscVal MoSold YrSold \\\n", + "0 Lvl AllPub ... MnPrv none 0 6 2010 \n", + "1 Lvl AllPub ... no_fence Gar2 12500 6 2010 \n", + "2 Lvl AllPub ... MnPrv none 0 3 2010 \n", + "3 Lvl AllPub ... no_fence none 0 6 2010 \n", + "4 HLS AllPub ... no_fence none 0 1 2010 \n", + "\n", + " SaleType SaleCondition istrain SalePrice SalePrice_est \n", + "0 WD Normal 0 NaN 104276.20 \n", + "1 WD Normal 0 NaN 152193.39 \n", + "2 WD Normal 0 NaN 184036.83 \n", + "3 WD Normal 0 NaN 198846.05 \n", + "4 WD Normal 0 NaN 209716.79 \n", + "\n", + "[5 rows x 83 columns]" + ], + "text/html": [ + "\n", + "
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IdMSSubClassMSZoningLotFrontageLotAreaStreetAlleyLotShapeLandContourUtilities...FenceMiscFeatureMiscValMoSoldYrSoldSaleTypeSaleConditionistrainSalePriceSalePrice_est
0146120RH80.011622Paveno_accessRegLvlAllPub...MnPrvnone062010WDNormal0NaN104276.20
1146220RL81.014267Paveno_accessIR1LvlAllPub...no_fenceGar21250062010WDNormal0NaN152193.39
2146360RL74.013830Paveno_accessIR1LvlAllPub...MnPrvnone032010WDNormal0NaN184036.83
3146460RL78.09978Paveno_accessIR1LvlAllPub...no_fencenone062010WDNormal0NaN198846.05
41465120RL43.05005Paveno_accessIR1HLSAllPub...no_fencenone012010WDNormal0NaN209716.79
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IdMSSubClassMSZoningLotFrontageLotAreaStreetAlleyLotShapeLandContourUtilities...FenceMiscFeatureMiscValMoSoldYrSoldSaleTypeSaleConditionistrainSalePriceSalePrice_est
0146120RH80.011622Paveno_accessRegLvlAllPub...MnPrvnone062010WDNormal0104276.20104276.20
1146220RL81.014267Paveno_accessIR1LvlAllPub...no_fenceGar21250062010WDNormal0152193.39152193.39
2146360RL74.013830Paveno_accessIR1LvlAllPub...MnPrvnone032010WDNormal0184036.83184036.83
3146460RL78.09978Paveno_accessIR1LvlAllPub...no_fencenone062010WDNormal0198846.05198846.05
41465120RL43.05005Paveno_accessIR1HLSAllPub...no_fencenone012010WDNormal0209716.79209716.79
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IdMSSubClassMSZoningLotFrontageLotAreaStreetAlleyLotShapeLandContourUtilities...FenceMiscFeatureMiscValMoSoldYrSoldSaleTypeSaleConditionistrainSalePriceSalePrice_est
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1220RL80.09600Paveno_accessRegLvlAllPub...no_fencenone052007WDNormal1181500.0NaN
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3470RL60.09550Paveno_accessIR1LvlAllPub...no_fencenone022006WDAbnorml1140000.0NaN
4560RL84.014260Paveno_accessIR1LvlAllPub...no_fencenone0122008WDNormal1250000.0NaN
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Presently they have around several hundred thousands of registered customers and serve almost one million consumers a year. They sell products from 5 major categories: wines, rare meat products, exotic fruits, specially prepared fish and sweet products. These can further be divided into gold and regular products. The customers can order and acquire products through 3 sales channels: physical stores, catalogs and the company’s website. Globally, the company had solid revenues and a healthy bottom line in the past 3 years, but the profit growth perspectives for the next 3 years are not promising... For this reason, several strategic initiatives are being considered to invert this situation. One is to improve the performance of marketing activities, with a special focus on marketing campaigns.\n", + "\n", + "**The Marketing Department**\n", + "\n", + "The marketing department was pressured to spend its annual budget more wisely. The CMO perceives the importance of having a more quantitative approach when taking decisions, reason why a small team of data scientists was hired with a clear objective in mind: to build a solution which will support direct marketing initiatives. Desirably, the success of these activities will prove the value of the approach and convince the more skeptical within the company\n", + "\n", + "**The Objective**\n", + "\n", + "The objective of the team is to build an analysis to address the highest profit for the next direct marketing campaign, scheduled for the next month. The new campaign, sixth, aims at selling a new gadget to the Customer Database. To build the analysis, a pilot campaign involving 2.240 customers was carried out. The customers were selected at random and contacted by phone regarding the acquisition of the gadget. During the following months, customers who bought the\n", + "offer were properly labeled. The total cost of the sample campaign was 6.720MU and the revenue generated by the customers who accepted the offer was 3.674MU. Globally the campaign had a profit of -3.046MU. The success rate of the campaign was 15%.\n", + "\n" + ], + "metadata": { + "id": "PemOd0DeT9hb" + } + }, + { + "cell_type": "markdown", + "source": [ + "##**SOLUÇÃO:**" + ], + "metadata": { + "id": "hGqXx8oVYPeT" + } + }, + { + "cell_type": "markdown", + "source": [ + "###***Importanto os dados e algumas bibliotecas***" + ], + "metadata": { + "id": "XuHJ7E9C0uJF" + } + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "id": "BpFBNbrtR8Gz" + }, + "outputs": [], + "source": [ + "## Importando as bibliotecas que eu vou usar\n", + "\n", + "import numpy as np\n", + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns" + ] + }, + { + "cell_type": "code", + "source": [ + "## Importando a base e criando um dataframe df\n", + "\n", + "df=pd.read_csv('https://raw.githubusercontent.com/ifood/ifood-data-analyst-case/main/retail_case_data.csv')" + ], + "metadata": { + "id": "V77pOoVRS1PI" + }, + "execution_count": 2, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "## Vendo se deu tudo certo\n", + "\n", + "df" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 0 + }, + "id": "l6wJLnQ9ZMBU", + "outputId": "2a1a6b24-fa4d-40d2-f5d9-afe18fa12b1e" + }, + "execution_count": 3, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + " ID Year_Birth Education Marital_Status Income Kidhome \\\n", + "0 5524 1957 Graduation Single 58138.0 0 \n", + "1 2174 1954 Graduation Single 46344.0 1 \n", + "2 4141 1965 Graduation Together 71613.0 0 \n", + "3 6182 1984 Graduation Together 26646.0 1 \n", + "4 5324 1981 PhD Married 58293.0 1 \n", + "... ... ... ... ... ... ... \n", + "2235 10870 1967 Graduation Married 61223.0 0 \n", + "2236 4001 1946 PhD Together 64014.0 2 \n", + "2237 7270 1981 Graduation Divorced 56981.0 0 \n", + "2238 8235 1956 Master Together 69245.0 0 \n", + "2239 9405 1954 PhD Married 52869.0 1 \n", + "\n", + " Teenhome Dt_Customer Recency MntWines ... NumWebVisitsMonth \\\n", + "0 0 2012-09-04 58 635 ... 7 \n", + "1 1 2014-03-08 38 11 ... 5 \n", + "2 0 2013-08-21 26 426 ... 4 \n", + "3 0 2014-02-10 26 11 ... 6 \n", + "4 0 2014-01-19 94 173 ... 5 \n", + "... ... ... ... ... ... ... \n", + "2235 1 2013-06-13 46 709 ... 5 \n", + "2236 1 2014-06-10 56 406 ... 7 \n", + "2237 0 2014-01-25 91 908 ... 6 \n", + "2238 1 2014-01-24 8 428 ... 3 \n", + "2239 1 2012-10-15 40 84 ... 7 \n", + "\n", + " AcceptedCmp3 AcceptedCmp4 AcceptedCmp5 AcceptedCmp1 AcceptedCmp2 \\\n", + "0 0 0 0 0 0 \n", + "1 0 0 0 0 0 \n", + "2 0 0 0 0 0 \n", + "3 0 0 0 0 0 \n", + "4 0 0 0 0 0 \n", + "... ... ... ... ... ... \n", + "2235 0 0 0 0 0 \n", + "2236 0 0 0 1 0 \n", + "2237 0 1 0 0 0 \n", + "2238 0 0 0 0 0 \n", + "2239 0 0 0 0 0 \n", + "\n", + " Complain Z_CostContact Z_Revenue Response \n", + "0 0 3 11 1 \n", + "1 0 3 11 0 \n", + "2 0 3 11 0 \n", + "3 0 3 11 0 \n", + "4 0 3 11 0 \n", + "... ... ... ... ... \n", + "2235 0 3 11 0 \n", + "2236 0 3 11 0 \n", + "2237 0 3 11 0 \n", + "2238 0 3 11 0 \n", + "2239 0 3 11 1 \n", + "\n", + "[2240 rows x 29 columns]" + ], + "text/html": [ + "\n", + "
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IDYear_BirthEducationMarital_StatusIncomeKidhomeTeenhomeDt_CustomerRecencyMntWines...NumWebVisitsMonthAcceptedCmp3AcceptedCmp4AcceptedCmp5AcceptedCmp1AcceptedCmp2ComplainZ_CostContactZ_RevenueResponse
055241957GraduationSingle58138.0002012-09-0458635...70000003111
121741954GraduationSingle46344.0112014-03-083811...50000003110
241411965GraduationTogether71613.0002013-08-2126426...40000003110
361821984GraduationTogether26646.0102014-02-102611...60000003110
453241981PhDMarried58293.0102014-01-1994173...50000003110
..................................................................
2235108701967GraduationMarried61223.0012013-06-1346709...50000003110
223640011946PhDTogether64014.0212014-06-1056406...70001003110
223772701981GraduationDivorced56981.0002014-01-2591908...60100003110
223882351956MasterTogether69245.0012014-01-248428...30000003110
223994051954PhDMarried52869.0112012-10-154084...70000003111
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2240 rows × 29 columns

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\n", + "
\n", + " " + ] + }, + "metadata": {}, + "execution_count": 3 + } + ] + }, + { + "cell_type": "code", + "source": [ + "## Ok, a visualização deu certo, temos 2240 linhas e 29 colunas. \n", + "## Agora vamos começar a análise exploratória e descritiva dos dados." + ], + "metadata": { + "id": "T1kp9TtHZa7M" + }, + "execution_count": 4, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "###***Análise exploratória***" + ], + "metadata": { + "id": "SNCXUTJDaOaM" + } + }, + { + "cell_type": "code", + "source": [ + "## Vendo as colunas do data frame para identificar com quais dados estamos trabalhando\n", + "df.columns" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "8AU1dNwEaM1Q", + "outputId": "23769625-07bf-4ced-9682-f7bc988e5540" + }, + "execution_count": 5, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "Index(['ID', 'Year_Birth', 'Education', 'Marital_Status', 'Income', 'Kidhome',\n", + " 'Teenhome', 'Dt_Customer', 'Recency', 'MntWines', 'MntFruits',\n", + " 'MntMeatProducts', 'MntFishProducts', 'MntSweetProducts',\n", + " 'MntGoldProds', 'NumDealsPurchases', 'NumWebPurchases',\n", + " 'NumCatalogPurchases', 'NumStorePurchases', 'NumWebVisitsMonth',\n", + " 'AcceptedCmp3', 'AcceptedCmp4', 'AcceptedCmp5', 'AcceptedCmp1',\n", + " 'AcceptedCmp2', 'Complain', 'Z_CostContact', 'Z_Revenue', 'Response'],\n", + " dtype='object')" + ] + }, + "metadata": {}, + "execution_count": 5 + } + ] + }, + { + "cell_type": "code", + "source": [ + "## Ok, é exatamente o que está descrito no PDF do desafio. " + ], + "metadata": { + "id": "76R6k-NKZdmp" + }, + "execution_count": 6, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "## Para entender melhor nossos dados, o comando abaixo nos mostra a quantidade de valores não nulos de cada coluna e qual o tipo de informação que está armazenada (texto, int e etc)\n", + "df.info()" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "nuzjDRkmJ3Er", + "outputId": "f6f2c40d-b5f2-40a6-ed7e-2c79ffc89121" + }, + "execution_count": 7, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "\n", + "RangeIndex: 2240 entries, 0 to 2239\n", + "Data columns (total 29 columns):\n", + " # Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 ID 2240 non-null int64 \n", + " 1 Year_Birth 2240 non-null int64 \n", + " 2 Education 2240 non-null object \n", + " 3 Marital_Status 2240 non-null object \n", + " 4 Income 2216 non-null float64\n", + " 5 Kidhome 2240 non-null int64 \n", + " 6 Teenhome 2240 non-null int64 \n", + " 7 Dt_Customer 2240 non-null object \n", + " 8 Recency 2240 non-null int64 \n", + " 9 MntWines 2240 non-null int64 \n", + " 10 MntFruits 2240 non-null int64 \n", + " 11 MntMeatProducts 2240 non-null int64 \n", + " 12 MntFishProducts 2240 non-null int64 \n", + " 13 MntSweetProducts 2240 non-null int64 \n", + " 14 MntGoldProds 2240 non-null int64 \n", + " 15 NumDealsPurchases 2240 non-null int64 \n", + " 16 NumWebPurchases 2240 non-null int64 \n", + " 17 NumCatalogPurchases 2240 non-null int64 \n", + " 18 NumStorePurchases 2240 non-null int64 \n", + " 19 NumWebVisitsMonth 2240 non-null int64 \n", + " 20 AcceptedCmp3 2240 non-null int64 \n", + " 21 AcceptedCmp4 2240 non-null int64 \n", + " 22 AcceptedCmp5 2240 non-null int64 \n", + " 23 AcceptedCmp1 2240 non-null int64 \n", + " 24 AcceptedCmp2 2240 non-null int64 \n", + " 25 Complain 2240 non-null int64 \n", + " 26 Z_CostContact 2240 non-null int64 \n", + " 27 Z_Revenue 2240 non-null int64 \n", + " 28 Response 2240 non-null int64 \n", + "dtypes: float64(1), int64(25), object(3)\n", + "memory usage: 507.6+ KB\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "## Confirmando a quantidade de informações nulas de cada coluna, talvez vamos precisar disso depois.\n", + "df.isnull().sum()" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "g75Wh_rJY9OE", + "outputId": "8e4c25c2-eb91-4031-a240-447d58450745" + }, + "execution_count": 8, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "ID 0\n", + "Year_Birth 0\n", + "Education 0\n", + "Marital_Status 0\n", + "Income 24\n", + "Kidhome 0\n", + "Teenhome 0\n", + "Dt_Customer 0\n", + "Recency 0\n", + "MntWines 0\n", + "MntFruits 0\n", + "MntMeatProducts 0\n", + "MntFishProducts 0\n", + "MntSweetProducts 0\n", + "MntGoldProds 0\n", + "NumDealsPurchases 0\n", + "NumWebPurchases 0\n", + "NumCatalogPurchases 0\n", + "NumStorePurchases 0\n", + "NumWebVisitsMonth 0\n", + "AcceptedCmp3 0\n", + "AcceptedCmp4 0\n", + "AcceptedCmp5 0\n", + "AcceptedCmp1 0\n", + "AcceptedCmp2 0\n", + "Complain 0\n", + "Z_CostContact 0\n", + "Z_Revenue 0\n", + "Response 0\n", + "dtype: int64" + ] + }, + "metadata": {}, + "execution_count": 8 + } + ] + }, + { + "cell_type": "code", + "source": [ + "## 24 nulos na coluna da renda, depois vamos aplicar um filtro para esses dados" + ], + "metadata": { + "id": "RS6xvXLGZMQe" + }, + "execution_count": 9, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "## Resumo estatístico das variáveis que não são dummies, texto ou data\n", + "\n", + "df[['Year_Birth','Income','Kidhome','Teenhome','Recency','MntWines','MntFruits','MntMeatProducts','MntFishProducts','MntSweetProducts','MntGoldProds', 'NumDealsPurchases', 'NumWebPurchases',\n", + " 'NumCatalogPurchases', 'NumStorePurchases', 'NumWebVisitsMonth', 'Z_CostContact', 'Z_Revenue']].describe().round(2)" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 0 + }, + "id": "gc-XmGvaOuz_", + "outputId": "4fa81b9c-0a64-400a-925d-0ba1444241a0" + }, + "execution_count": 10, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + " Year_Birth Income Kidhome Teenhome Recency MntWines MntFruits \\\n", + "count 2240.00 2216.00 2240.00 2240.00 2240.00 2240.00 2240.00 \n", + "mean 1968.81 52247.25 0.44 0.51 49.11 303.94 26.30 \n", + "std 11.98 25173.08 0.54 0.54 28.96 336.60 39.77 \n", + "min 1893.00 1730.00 0.00 0.00 0.00 0.00 0.00 \n", + "25% 1959.00 35303.00 0.00 0.00 24.00 23.75 1.00 \n", + "50% 1970.00 51381.50 0.00 0.00 49.00 173.50 8.00 \n", + "75% 1977.00 68522.00 1.00 1.00 74.00 504.25 33.00 \n", + "max 1996.00 666666.00 2.00 2.00 99.00 1493.00 199.00 \n", + "\n", + " MntMeatProducts MntFishProducts MntSweetProducts MntGoldProds \\\n", + "count 2240.00 2240.00 2240.00 2240.00 \n", + "mean 166.95 37.53 27.06 44.02 \n", + "std 225.72 54.63 41.28 52.17 \n", + "min 0.00 0.00 0.00 0.00 \n", + "25% 16.00 3.00 1.00 9.00 \n", + "50% 67.00 12.00 8.00 24.00 \n", + "75% 232.00 50.00 33.00 56.00 \n", + "max 1725.00 259.00 263.00 362.00 \n", + "\n", + " NumDealsPurchases NumWebPurchases NumCatalogPurchases \\\n", + "count 2240.00 2240.00 2240.00 \n", + "mean 2.33 4.08 2.66 \n", + "std 1.93 2.78 2.92 \n", + "min 0.00 0.00 0.00 \n", + "25% 1.00 2.00 0.00 \n", + "50% 2.00 4.00 2.00 \n", + "75% 3.00 6.00 4.00 \n", + "max 15.00 27.00 28.00 \n", + "\n", + " NumStorePurchases NumWebVisitsMonth Z_CostContact Z_Revenue \n", + "count 2240.00 2240.00 2240.0 2240.0 \n", + "mean 5.79 5.32 3.0 11.0 \n", + "std 3.25 2.43 0.0 0.0 \n", + "min 0.00 0.00 3.0 11.0 \n", + "25% 3.00 3.00 3.0 11.0 \n", + "50% 5.00 6.00 3.0 11.0 \n", + "75% 8.00 7.00 3.0 11.0 \n", + "max 13.00 20.00 3.0 11.0 " + ], + "text/html": [ + "\n", + "
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Year_BirthIncomeKidhomeTeenhomeRecencyMntWinesMntFruitsMntMeatProductsMntFishProductsMntSweetProductsMntGoldProdsNumDealsPurchasesNumWebPurchasesNumCatalogPurchasesNumStorePurchasesNumWebVisitsMonthZ_CostContactZ_Revenue
count2240.002216.002240.002240.002240.002240.002240.002240.002240.002240.002240.002240.002240.002240.002240.002240.002240.02240.0
mean1968.8152247.250.440.5149.11303.9426.30166.9537.5327.0644.022.334.082.665.795.323.011.0
std11.9825173.080.540.5428.96336.6039.77225.7254.6341.2852.171.932.782.923.252.430.00.0
min1893.001730.000.000.000.000.000.000.000.000.000.000.000.000.000.000.003.011.0
25%1959.0035303.000.000.0024.0023.751.0016.003.001.009.001.002.000.003.003.003.011.0
50%1970.0051381.500.000.0049.00173.508.0067.0012.008.0024.002.004.002.005.006.003.011.0
75%1977.0068522.001.001.0074.00504.2533.00232.0050.0033.0056.003.006.004.008.007.003.011.0
max1996.00666666.002.002.0099.001493.00199.001725.00259.00263.00362.0015.0027.0028.0013.0020.003.011.0
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\n", + " " + ] + }, + "metadata": {}, + "execution_count": 10 + } + ] + }, + { + "cell_type": "code", + "source": [ + "## Observações extraídas até então:\n", + "### Coluna de ano de nascimento: algum cliente inseriu data de nascimento em 1893, mas isso não faz sentido, iremos verificar depois.\n", + "### Coluna de renda: o valor mínimo dessa coluna (1730) e o valor máximo (666666) também estão destoando bastante da média e da mediana da renda. Verificar quem são esses cliente depois.\n", + "### Número de compras com descontos: o máximo é bem maior do que a média e a mediana, 15 compras. Talvez essa informação esteja errada.\n", + "## As colunas de custo do contrato e de receita são valores constantes, então possivelmente podemos \"dropá-las\"" + ], + "metadata": { + "id": "L0b4uDcpZnhy" + }, + "execution_count": 11, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "## Agora, vamos ver quais são as categorias existentes dentro das variáveis categóricas:\n", + "df['Marital_Status'].unique()" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "6WqjQ1nbWIsa", + "outputId": "827d900d-6c77-4a12-c5ac-1307f9964630" + }, + "execution_count": 12, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "array(['Single', 'Together', 'Married', 'Divorced', 'Widow', 'Alone',\n", + " 'Absurd', 'YOLO'], dtype=object)" + ] + }, + "metadata": {}, + "execution_count": 12 + } + ] + }, + { + "cell_type": "code", + "source": [ + "## As categorias \"YOLO\" e \"Absurd\" não condizem com a informação que queremos. Provavelmente foi alguma \"zoeira\" de um usuário, portanto podemos deletá-las. \n", + "## A categoria Alone também não faz muito sentido, talvez podemos incluir suas observações na single, divorced ou widow." + ], + "metadata": { + "id": "u7ojNgvCdlfw" + }, + "execution_count": 13, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "df['Education'].unique()" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "YSd_gZReXH7c", + "outputId": "594a1b57-0eb7-4524-8d42-5304658cfd90" + }, + "execution_count": 14, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "array(['Graduation', 'PhD', 'Master', 'Basic', '2n Cycle'], dtype=object)" + ] + }, + "metadata": {}, + "execution_count": 14 + } + ] + }, + { + "cell_type": "code", + "source": [ + "## Considerando que esses níveis de graduação são estadunidenses, temos que:\n", + "## 1) \"2n Cycle\" é o mesmo que o nível de \"Master\". Logo, vamos realizar essa substituição.\n", + "## 2) O nível chamado de \"graduation\", nos EUA, não é o mesmo que o nível de graduação no Brasil. Ele faz referência a estudantes com pós-graduação, mestrado ou doutorado.\n", + "## Como neste caso não sabemos se os estudantes de graduation são apenas os estudantes de pós ou se neles também estão incluídos os demais tipos (mestrado e doutorado), vou mantê-los de forma separada.\n", + "\n", + "## referência: https://www.estudarfora.org.br/graduate-e-undergraduate-diferenca/#:~:text=Nos%20Estados%20Unidos%2C%20os%20undergraduate,um%20curso%20de%20n%C3%ADvel%20undergraduate." + ], + "metadata": { + "id": "0T7zgD3ieASV" + }, + "execution_count": 15, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "## Agora, vamos analisar variável de \"Data de cliente\" para entender mais sobre a base e sobre o intervalo temporal.\n", + "df['Dt_Customer'].min()" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 0 + }, + "id": "k_vwXj2DXlPq", + "outputId": "965f342c-858d-4bfe-dbce-169017d2737e" + }, + "execution_count": 16, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "'2012-07-30'" + ], + "application/vnd.google.colaboratory.intrinsic+json": { + "type": "string" + } + }, + "metadata": {}, + "execution_count": 16 + } + ] + }, + { + "cell_type": "code", + "source": [ + "df['Dt_Customer'].max()" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 0 + }, + "id": "IJHZ9Ka6Yury", + "outputId": "249076de-8692-43e5-f126-072c64e1b814" + }, + "execution_count": 17, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "'2014-06-29'" + ], + "application/vnd.google.colaboratory.intrinsic+json": { + "type": "string" + } + }, + "metadata": {}, + "execution_count": 17 + } + ] + }, + { + "cell_type": "code", + "source": [ + "## As datas indicam que a base começou em 2012 e termina em 2014. Isso é importante para nossa análise tbm." + ], + "metadata": { + "id": "fH9NhMUPeFp6" + }, + "execution_count": 18, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "## Comentário final da AE:\n", + "\n", + "### Dropar colunas com var. constantes.\n", + "### Tratar dados da coluna de ano de nascimento (min).\n", + "### Tratar dados da coluna de estado civil (yolo, absurd, alone).\n", + "## Tratar dado da coluna de educação (2n cycle=Master)\n", + "### Tratar dados da renda: valor máximo, analisar valores mínimos e possivelmente substituir nulos.\n", + "### Avaliar coluna de compras com descontos: o máximo é bem maior do que a média e a mediana, 15 compras.\n" + ], + "metadata": { + "id": "AovdXspHP7Yr" + }, + "execution_count": 19, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "### **Tratando os dados de acordo com o que foi identificado na AE**" + ], + "metadata": { + "id": "4BbPxrLrKWtp" + } + }, + { + "cell_type": "markdown", + "source": [ + "#### **Tratamentos iniciais (drops e substituições, exceto da income)**" + ], + "metadata": { + "id": "KgOKww7jhGyR" + } + }, + { + "cell_type": "code", + "source": [ + "## primeiro, vamos apagar as colunas de variáveis constantes que não iremos utilizar (custo=3 e receita=11)" + ], + "metadata": { + "id": "vR1dS5DInrX3" + }, + "execution_count": 20, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "df.drop(columns=['Z_CostContact','Z_Revenue'],inplace=True)" + ], + "metadata": { + "id": "7VtOolHqnCFB" + }, + "execution_count": 21, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "df" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 0 + }, + "id": "PfcpDT5ToBcY", + "outputId": "cddb8d96-25da-436f-9a2e-a31bcfd8b939" + }, + "execution_count": 22, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + " ID Year_Birth Education Marital_Status Income Kidhome \\\n", + "0 5524 1957 Graduation Single 58138.0 0 \n", + "1 2174 1954 Graduation Single 46344.0 1 \n", + "2 4141 1965 Graduation Together 71613.0 0 \n", + "3 6182 1984 Graduation Together 26646.0 1 \n", + "4 5324 1981 PhD Married 58293.0 1 \n", + "... ... ... ... ... ... ... \n", + "2235 10870 1967 Graduation Married 61223.0 0 \n", + "2236 4001 1946 PhD Together 64014.0 2 \n", + "2237 7270 1981 Graduation Divorced 56981.0 0 \n", + "2238 8235 1956 Master Together 69245.0 0 \n", + "2239 9405 1954 PhD Married 52869.0 1 \n", + "\n", + " Teenhome Dt_Customer Recency MntWines ... NumCatalogPurchases \\\n", + "0 0 2012-09-04 58 635 ... 10 \n", + "1 1 2014-03-08 38 11 ... 1 \n", + "2 0 2013-08-21 26 426 ... 2 \n", + "3 0 2014-02-10 26 11 ... 0 \n", + "4 0 2014-01-19 94 173 ... 3 \n", + "... ... ... ... ... ... ... \n", + "2235 1 2013-06-13 46 709 ... 3 \n", + "2236 1 2014-06-10 56 406 ... 2 \n", + "2237 0 2014-01-25 91 908 ... 3 \n", + "2238 1 2014-01-24 8 428 ... 5 \n", + "2239 1 2012-10-15 40 84 ... 1 \n", + "\n", + " NumStorePurchases NumWebVisitsMonth AcceptedCmp3 AcceptedCmp4 \\\n", + "0 4 7 0 0 \n", + "1 2 5 0 0 \n", + "2 10 4 0 0 \n", + "3 4 6 0 0 \n", + "4 6 5 0 0 \n", + "... ... ... ... ... \n", + "2235 4 5 0 0 \n", + "2236 5 7 0 0 \n", + "2237 13 6 0 1 \n", + "2238 10 3 0 0 \n", + "2239 4 7 0 0 \n", + "\n", + " AcceptedCmp5 AcceptedCmp1 AcceptedCmp2 Complain Response \n", + "0 0 0 0 0 1 \n", + "1 0 0 0 0 0 \n", + "2 0 0 0 0 0 \n", + "3 0 0 0 0 0 \n", + "4 0 0 0 0 0 \n", + "... ... ... ... ... ... \n", + "2235 0 0 0 0 0 \n", + "2236 0 1 0 0 0 \n", + "2237 0 0 0 0 0 \n", + "2238 0 0 0 0 0 \n", + "2239 0 0 0 0 1 \n", + "\n", + "[2240 rows x 27 columns]" + ], + "text/html": [ + "\n", + "
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055241957GraduationSingle58138.0002012-09-0458635...10470000001
121741954GraduationSingle46344.0112014-03-083811...1250000000
241411965GraduationTogether71613.0002013-08-2126426...21040000000
361821984GraduationTogether26646.0102014-02-102611...0460000000
453241981PhDMarried58293.0102014-01-1994173...3650000000
..................................................................
2235108701967GraduationMarried61223.0012013-06-1346709...3450000000
223640011946PhDTogether64014.0212014-06-1056406...2570001000
223772701981GraduationDivorced56981.0002014-01-2591908...31360100000
223882351956MasterTogether69245.0012014-01-248428...51030000000
223994051954PhDMarried52869.0112012-10-154084...1470000001
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IDYear_BirthEducationMarital_StatusIncomeKidhomeTeenhomeDt_CustomerRecencyMntWines...NumCatalogPurchasesNumStorePurchasesNumWebVisitsMonthAcceptedCmp3AcceptedCmp4AcceptedCmp5AcceptedCmp1AcceptedCmp2ComplainResponse
223394321977GraduationTogether666666.0102013-06-02239...1360000000
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IDYear_BirthEducationMarital_StatusIncomeKidhomeTeenhomeDt_CustomerRecencyMntWines...NumCatalogPurchasesNumStorePurchasesNumWebVisitsMonthAcceptedCmp3AcceptedCmp4AcceptedCmp5AcceptedCmp1AcceptedCmp2ComplainResponse
124568621971GraduationDivorced1730.0002014-05-18651...00200000000
2153761979GraduationMarried2447.0102013-01-06421...28010000000
1524111101973GraduationSingle3502.0102013-04-13562...00140000000
184699311963PhDMarried4023.0112014-06-23295...00190000000
1975103111969GraduationMarried4428.0012013-10-05016...0010000000
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2391100418932n CycleSingle60182.0012014-05-17238...0240000000
33911501899PhDTogether83532.0002013-09-2636755...6410010000
192782919002n CycleDivorced36640.0102013-09-269915...1250000010
195066631940PhDSingle51141.0002013-07-0896144...1450000000
42469321941PhDMarried93027.0002013-04-13771285...10520010000
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747105481995GraduationSingle71163.0002014-03-0930283...81210000000
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11701931996BasicMarried14421.0002014-02-17810...0251000000
46990919962n CycleMarried7500.0002012-11-09243...1390000001
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IDYear_BirthEducationMarital_StatusIncomeKidhomeTeenhomeDt_CustomerRecencyMntWines...NumCatalogPurchasesNumStorePurchasesNumWebVisitsMonthAcceptedCmp3AcceptedCmp4AcceptedCmp5AcceptedCmp1AcceptedCmp2ComplainResponse
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IDYear_BirthEducationMarital_StatusIncomeKidhomeTeenhomeDt_CustomerRecencyMntWines...NumCatalogPurchasesNumStorePurchasesNumWebVisitsMonthAcceptedCmp3AcceptedCmp4AcceptedCmp5AcceptedCmp1AcceptedCmp2ComplainResponse
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IDAcceptedCmp3AcceptedCmp4AcceptedCmp5AcceptedCmp1AcceptedCmp2Response
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IDYear_BirthEducationMarital_StatusIncomeKidhomeTeenhomeDt_CustomerRecencyMntWines...NumCatalogPurchasesNumStorePurchasesNumWebVisitsMonthAcceptedCmp3AcceptedCmp4AcceptedCmp5AcceptedCmp1AcceptedCmp2ComplainResponse
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82211961.0GraduationSingle57091.0002014-06-150464...3750000101
1834105211977.0GraduationMarried54809.0112013-09-11063...1540000001
74940731954.02n CycleMarried63564.0002014-01-290769...10761000001
108973481958.0PhDSingle71691.0002014-03-170336...7520000001
..................................................................
166941271967.0PhDMarried77766.0012013-02-22971004...101161000001
125251531967.0PhDMarried77766.0012013-02-22971004...101161000001
126139791983.0PhDDivorced90687.0002013-05-2298982...2820010001
69072301960.0PhDDivorced50611.0012012-10-0498459...5760100001
147340701969.0PhDMarried94871.0022012-09-0199169...5470110001
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..................................................................
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Quem sabe a informação não está triplicada.\n", + "df[(df['Recency']==3) & (df['Education']=='PhD')& (df['Year_Birth']==1973)]" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 0 + }, + "id": "UtbIsCH-mfjf", + "outputId": "286efc49-b812-44ca-b4c3-5e61723aae4d" + }, + "execution_count": 49, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + " ID Year_Birth Education Marital_Status Income Kidhome Teenhome \\\n", + "1595 1041 1973.0 PhD Single 48432.0 0 1 \n", + "2177 492 1973.0 PhD YOLO 48432.0 0 1 \n", + "2202 11133 1973.0 PhD YOLO 48432.0 0 1 \n", + "\n", + " Dt_Customer Recency MntWines ... NumCatalogPurchases \\\n", + "1595 2012-10-18 3 322 ... 1 \n", + "2177 2012-10-18 3 322 ... 1 \n", + "2202 2012-10-18 3 322 ... 1 \n", + "\n", + " NumStorePurchases NumWebVisitsMonth AcceptedCmp3 AcceptedCmp4 \\\n", + "1595 6 8 0 0 \n", + "2177 6 8 0 0 \n", + "2202 6 8 0 0 \n", + "\n", + " AcceptedCmp5 AcceptedCmp1 AcceptedCmp2 Complain Response \n", + "1595 0 0 0 0 1 \n", + "2177 0 0 0 0 0 \n", + "2202 0 0 0 0 1 \n", + "\n", + "[3 rows x 27 columns]" + ], + "text/html": [ + "\n", + "
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159510411973.0PhDSingle48432.0012012-10-183322...1680000001
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361821984.0GraduationTogether26646.0102014-02-102611...0460000000
453241981.0PhDMarried58293.0102014-01-1994173...3650000000
..................................................................
2235108701967.0GraduationMarried61223.0012013-06-1346709...3450000000
223640011946.0PhDTogether64014.0212014-06-1056406...2570001000
223772701981.0GraduationDivorced56981.0002014-01-2591908...31360100000
223882351956.0MasterTogether69245.0012014-01-248428...51030000000
223994051954.0PhDMarried52869.0112012-10-154084...1470000001
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IDYear_BirthEducationMarital_StatusIncomeKidhomeTeenhomeDt_CustomerRecencyMntWines...NumCatalogPurchasesNumStorePurchasesNumWebVisitsMonthAcceptedCmp3AcceptedCmp4AcceptedCmp5AcceptedCmp1AcceptedCmp2ComplainResponse
209377341993.0GraduationAbsurd79244.0002012-12-1958471...10710011001
213443691957.0MasterAbsurd65487.0002014-01-1048240...5620000000
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IDYear_BirthEducationMarital_StatusIncomeKidhomeTeenhomeDt_CustomerRecencyMntWines...NumCatalogPurchasesNumStorePurchasesNumWebVisitsMonthAcceptedCmp3AcceptedCmp4AcceptedCmp5AcceptedCmp1AcceptedCmp2ComplainResponse
7558461977.0GraduationDivorced40246.0102012-12-19682...0260000000
10836291978.0GraduationSingle38557.0102012-12-191776...1370000000
72051141965.0MasterMarried74806.0012012-12-191670...4540000000
209377341993.0GraduationAbsurd79244.0002012-12-1958471...10710011001
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166326391966.0GraduationSingle43602.0112014-01-104519...1260000000
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IDYear_BirthEducationMarital_StatusIncomeKidhomeTeenhomeDt_CustomerRecencyMntWines...NumCatalogPurchasesNumStorePurchasesNumWebVisitsMonthAcceptedCmp3AcceptedCmp4AcceptedCmp5AcceptedCmp1AcceptedCmp2ComplainResponse
055241957.0GraduationSingle58138.0002012-09-0458635...10470000001
121741954.0GraduationSingle46344.0112014-03-083811...1250000000
241411965.0GraduationTogether71613.0002013-08-2126426...21040000000
361821984.0GraduationTogether26646.0102014-02-102611...0460000000
453241981.0PhDMarried58293.0102014-01-1994173...3650000000
..................................................................
2235108701967.0GraduationMarried61223.0012013-06-1346709...3450000000
223640011946.0PhDTogether64014.0212014-06-1056406...2570001000
223772701981.0GraduationDivorced56981.0002014-01-2591908...31360100000
223882351956.0MasterTogether69245.0012014-01-248428...51030000000
223994051954.0PhDMarried52869.0112012-10-154084...1470000001
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2237 rows × 27 columns

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count11.0011.011.011.0011.0011.0011.0011.0011.0011.00...11.0011.0011.0011.011.0011.0011.0011.0011.011.00
mean69805.270.00.047.64384.0050.27353.64116.1845.8280.45...5.096.642.820.00.090.270.180.090.00.27
std18634.250.00.022.55286.5257.73258.3789.8338.4077.98...3.483.321.940.00.300.470.400.300.00.47
min34824.000.00.011.004.000.0011.002.000.004.00...0.002.001.000.00.000.000.000.000.00.00
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75%79689.000.00.065.50445.5073.50462.50197.0077.50110.50...7.508.504.000.00.000.500.000.000.00.50
max95529.000.00.067.00966.00152.00890.00250.00106.00245.00...10.0012.006.000.01.001.001.001.000.01.00
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41750671994.0GraduationTogether80134.0002014-02-1411966...71150111100
116369051994.0GraduationTogether80685.0002012-08-2255241...41020000000
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1314331958.0MasterAlone61331.0112013-03-1042534...1680000000
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IDYear_BirthEducationMarital_StatusIncomeKidhomeTeenhomeDt_CustomerRecencyMntWines...NumCatalogPurchasesNumStorePurchasesNumWebVisitsMonthAcceptedCmp3AcceptedCmp4AcceptedCmp5AcceptedCmp1AcceptedCmp2ComplainResponse
13876601973.0PhDAlone35860.0112014-05-193715...1251000001
106320551973.0PhDDivorced35860.0112014-05-193715...1251000000
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13914611965.0PhDDivorced36921.0112013-07-287417...0370000000
32282751965.0PhDDivorced47025.0112014-02-09616...0270000000
38997991968.0PhDDivorced83664.0112013-05-0857866...21250000000
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1409100101953.0PhDDivorced36957.0112012-09-0643100...2290000001
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count14.0014.0014.0014.014.0014.0014.0014.0014.0014.00...14.0014.0014.0014.0014.014.014.014.014.014.00
mean6051.001964.1445227.291.01.0740.36115.574.0035.077.36...0.713.576.500.210.00.00.00.00.00.14
std3155.0010.6314223.400.00.2729.32226.006.9344.618.82...0.732.591.560.430.00.00.00.00.00.36
min1461.001948.0034487.001.01.001.008.000.002.000.00...0.002.005.000.000.00.00.00.00.00.00
25%3823.501954.0035881.501.01.0014.2515.000.008.002.25...0.002.005.000.000.00.00.00.00.00.00
50%5642.501966.5039197.001.01.0037.0018.000.5014.004.00...1.003.006.500.000.00.00.00.00.00.00
75%8736.251973.0046536.751.01.0063.00133.004.2538.2510.00...1.003.757.750.000.00.00.00.00.00.00
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055241957.0GraduationSingle58138.0002012-09-0458635...10470000001
121741954.0GraduationSingle46344.0112014-03-083811...1250000000
241411965.0GraduationTogether71613.0002013-08-2126426...21040000000
361821984.0GraduationTogether26646.0102014-02-102611...0460000000
453241981.0PhDMarried58293.0102014-01-1994173...3650000000
..................................................................
2235108701967.0GraduationMarried61223.0012013-06-1346709...3450000000
223640011946.0PhDTogether64014.0212014-06-1056406...2570001000
223772701981.0GraduationDivorced56981.0002014-01-2591908...31360100000
223882351956.0MasterTogether69245.0012014-01-248428...51030000000
223994051954.0PhDMarried52869.0112012-10-154084...1470000001
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055241957.0GraduationSingle58138.0002012-09-0458635...10470000001
121741954.0GraduationSingle46344.0112014-03-083811...1250000000
241411965.0GraduationTogether71613.0002013-08-2126426...21040000000
361821984.0GraduationTogether26646.0102014-02-102611...0460000000
453241981.0PhDMarried58293.0102014-01-1994173...3650000000
..................................................................
2235108701967.0GraduationMarried61223.0012013-06-1346709...3450000000
223640011946.0PhDTogether64014.0212014-06-1056406...2570001000
223772701981.0GraduationDivorced56981.0002014-01-2591908...31360100000
223882351956.0MasterTogether69245.0012014-01-248428...51030000000
223994051954.0PhDMarried52869.0112012-10-154084...1470000001
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IDYear_BirthEducationMarital_StatusIncomeKidhomeTeenhomeDt_CustomerRecencyMntWines...NumCatalogPurchasesNumStorePurchasesNumWebVisitsMonthAcceptedCmp3AcceptedCmp4AcceptedCmp5AcceptedCmp1AcceptedCmp2ComplainResponse
1314331958.0MasterAlone61331.0112013-03-1042534...1680000000
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IDYear_BirthIncomeKidhomeTeenhomeRecencyMntWinesMntFruitsMntMeatProductsMntFishProducts...NumCatalogPurchasesNumStorePurchasesNumWebVisitsMonthAcceptedCmp3AcceptedCmp4AcceptedCmp5AcceptedCmp1AcceptedCmp2ComplainResponse
count9.009.009.009.09.09.009.009.009.009.00...9.009.009.009.09.09.09.09.09.09.00
mean4527.441965.8945831.781.01.035.11144.004.7868.1110.11...1.674.336.780.00.00.00.00.00.00.33
std2615.409.178869.730.00.023.78115.345.5944.4814.35...1.801.730.970.00.00.00.00.00.00.50
min675.001950.0029435.001.01.02.0023.000.009.000.00...0.002.006.000.00.00.00.00.00.00.00
25%2715.001963.0042835.001.01.020.0070.002.0037.003.00...1.003.006.000.00.00.00.00.00.00.00
50%5320.001971.0044635.001.01.025.0081.003.0058.006.00...1.004.007.000.00.00.00.00.00.00.00
75%5684.001973.0052034.001.01.053.00229.007.0093.0010.00...2.005.007.000.00.00.00.00.00.01.00
max8180.001974.0059354.001.01.067.00379.0018.00140.0047.00...6.008.009.000.00.00.00.00.00.01.00
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IDYear_BirthIncomeKidhomeTeenhomeRecencyMntWinesMntFruitsMntMeatProductsMntFishProducts...NumCatalogPurchasesNumStorePurchasesNumWebVisitsMonthAcceptedCmp3AcceptedCmp4AcceptedCmp5AcceptedCmp1AcceptedCmp2ComplainResponse
count2.002.002.002.02.02.002.002.002.002.00...2.02.002.002.02.02.02.02.02.02.00
mean6743.501957.0049480.001.01.028.50136.0010.5033.5011.50...2.03.506.000.00.00.00.00.00.00.50
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Income
Marital_StatusEducation
DivorcedBasic9548.00
Graduation54526.04
Master50159.03
PhD53786.08
MarriedBasic21960.50
Graduation50800.26
Master50686.06
PhD58138.03
SingleBasic18238.67
Graduation51435.23
Master53577.06
PhD53314.61
TogetherBasic21240.07
Graduation53607.40
Master49495.94
PhD56041.42
WidowBasic22123.00
Graduation54976.66
Master56211.12
PhD60288.08
\n", + "
\n", + " \n", + " \n", + " \n", + "\n", + " \n", + "
\n", + "
\n", + " " + ] + }, + "metadata": {}, + "execution_count": 89 + } + ] + }, + { + "cell_type": "code", + "source": [ + "## Substituindo as 24 observações com renda nula manualmente:\n", + "## Single\n", + "df.loc[((df['Income'].isnull()) & (df['Marital_Status']=='Single') & (df['Education']=='Graduation')),'Income']= 51435.23\n", + "df.loc[((df['Income'].isnull()) & (df['Marital_Status']=='Single') & (df['Education']=='PhD')),'Income']= 53314.61\n", + "df.loc[((df['Income'].isnull()) & (df['Marital_Status']=='Single') & (df['Education']=='Master')),'Income']= 53577.06\n", + "## Married\n", + "df.loc[((df['Income'].isnull()) & (df['Marital_Status']=='Married') & (df['Education']=='Graduation')),'Income']= 50800.26\n", + "df.loc[((df['Income'].isnull()) & (df['Marital_Status']=='Married') & (df['Education']=='PhD')),'Income']= 58138.03\n", + "df.loc[((df['Income'].isnull()) & (df['Marital_Status']=='Married') & (df['Education']=='Master')),'Income']= 50686.06\n", + "## Together\n", + "df.loc[((df['Income'].isnull()) & (df['Marital_Status']=='Together') & (df['Education']=='Graduation')),'Income']= 53607.40\n", + "df.loc[((df['Income'].isnull()) & (df['Marital_Status']=='Together') & (df['Education']=='PhD')),'Income']= 56041.42\n", + "df.loc[((df['Income'].isnull()) & (df['Marital_Status']=='Together') & (df['Education']=='Master')),'Income']= 49495.94\n", + "## Widow\n", + "df.loc[((df['Income'].isnull()) & (df['Marital_Status']=='Widow') & (df['Education']=='Master')),'Income']= 56211.12" + ], + "metadata": { + "id": "KAGRDsBqVTqi" + }, + "execution_count": 90, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "## Provavelmenter tem uma forma mais eficiente de fazer esse comando usando o if, o for e o where, mas eu não consegui fazer.\n", + "## Vou tirar a dúvida depois com algum mentor da Awari.\n", + "## Verificando se deu certo\n", + "df['Income'].isnull().value_counts()" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "d9GMkhZiY3d7", + "outputId": "7db2cf8e-fbb5-456f-b94d-02d61272a2a1" + }, + "execution_count": 91, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "False 2233\n", + "Name: Income, dtype: int64" + ] + }, + "metadata": {}, + "execution_count": 91 + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "##### **Forma 2**" + ], + "metadata": { + "id": "lz5LZSWnq6LJ" + } + }, + { + "cell_type": "markdown", + "source": [ + "A forma dois consiste no seguinte: queremos substituir os dados nulos da renda de algumas observações por estimativas mais precisas oriundas de uma regressão linear ou linear com grau polinomial transformado. Para isso:\n", + "\n", + "1. Precisamos identificar quais seriam as variáveis ideias em um modelo para estimar a renda.\n", + "\n", + "2. Para descobrir essas variáveis, iremos analisar a correlação entre a renda e as variáveis disponíveis.\n", + "\n", + "3. Porém, algumas variáveis que podem ser relevantes ao modelo são categóricas e estão em formato de texto (Marital Status e Education). Nesses casos, precisamos transformar as strings em números para captar seus efeitos. Podemos fazer isso transformando a categoria em uma dummie ou em uma categoria codificada (encoding). \n", + "\n", + "4. Vai ser mais longo, mas para fazer um trabalho mais completo vou aplicar ambas as metodologias e adotar a que tiver o melhor resultado em termos de correlação, sendo válidos os seguintes graus de correlação:\n", + "\n", + "- \"Perfect: If the value is near ± 1, then it said to be a perfect correlation: as one variable increases, the other variable tends to also increase (if positive) or decrease (if negative).\n", + "\n", + "- High degree: If the coefficient value lies between ± 0.50 and ± 1, then it is said to be a strong correlation.\n", + "\n", + "- Moderate degree: If the value lies between ± 0.30 and ± 0.49, then it is said to be a medium correlation.\n", + "\n", + "- Low degree: When the value lies below + .29, then it is said to be a small correlation.\n", + "\n", + "- No correlation: When the value is zero.\"\n", + "\n", + "5. Definidas as variáveis com correlação aceitável para o modelo, vou salvá-las juntamente com suas correlações em um dataframe e depois vou estimar a regressão linear em bases separadas para treino e validação.\n", + "\n", + "6. Se a regressão linear não apresentar resultados satisfatórios de R² e erro, vou estimar uma regressão múltipla.\n", + "\n", + "7. Feito tudo isso, vou escolher o melhor modelo, estimar a regressão para o caso real e substituir os nulos." + ], + "metadata": { + "id": "JNxnzXdlrnKL" + } + }, + { + "cell_type": "code", + "source": [ + "## Verificando se no df_copy as observações continuam com renda nula\n", + "df_copy[df_copy['Income'].isnull()]" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 927 + }, + "id": "ASmMnFWbo-G6", + "outputId": "fca6a497-f4cb-4262-8e6b-9ac000274acd" + }, + "execution_count": 92, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + " ID Year_Birth Education Marital_Status Income Kidhome Teenhome \\\n", + "10 1994 1983.0 Graduation Married NaN 1 0 \n", + "27 5255 1986.0 Graduation Single NaN 1 0 \n", + "43 7281 1959.0 PhD Single NaN 0 0 \n", + "48 7244 1951.0 Graduation Single NaN 2 1 \n", + "58 8557 1982.0 Graduation Single NaN 1 0 \n", + "71 10629 1973.0 Master Married NaN 1 0 \n", + "90 8996 1957.0 PhD Married NaN 2 1 \n", + "91 9235 1957.0 Graduation Single NaN 1 1 \n", + "92 5798 1973.0 Master Together NaN 0 0 \n", + "128 8268 1961.0 PhD Married NaN 0 1 \n", + "133 1295 1963.0 Graduation Married NaN 0 1 \n", + "312 2437 1989.0 Graduation Married NaN 0 0 \n", + "319 2863 1970.0 Graduation Single NaN 1 2 \n", + "1379 10475 1970.0 Master Together NaN 0 1 \n", + "1382 2902 1958.0 Graduation Together NaN 1 1 \n", + "1383 4345 1964.0 Master Single NaN 1 1 \n", + "1386 3769 1972.0 PhD Together NaN 1 0 \n", + "2059 7187 1969.0 Master Together NaN 1 1 \n", + "2061 1612 1981.0 PhD Single NaN 1 0 \n", + "2078 5079 1971.0 Graduation Married NaN 1 1 \n", + "2079 10339 1954.0 Master Together NaN 0 1 \n", + "2081 3117 1955.0 Graduation Single NaN 0 1 \n", + "2084 5250 1943.0 Master Widow NaN 0 0 \n", + "2228 8720 1978.0 Master Together NaN 0 0 \n", + "2233 9432 1977.0 Graduation Together NaN 1 0 \n", + "\n", + " Dt_Customer Recency MntWines ... NumCatalogPurchases \\\n", + "10 2013-11-15 11 5 ... 0 \n", + "27 2013-02-20 19 5 ... 0 \n", + "43 2013-11-05 80 81 ... 3 \n", + "48 2014-01-01 96 48 ... 1 \n", + "58 2013-06-17 57 11 ... 0 \n", + "71 2012-09-14 25 25 ... 0 \n", + "90 2012-11-19 4 230 ... 2 \n", + "91 2014-05-27 45 7 ... 0 \n", + "92 2013-11-23 87 445 ... 4 \n", + "128 2013-07-11 23 352 ... 1 \n", + "133 2013-08-11 96 231 ... 5 \n", + "312 2013-06-03 69 861 ... 5 \n", + "319 2013-08-23 67 738 ... 3 \n", + "1379 2013-04-01 39 187 ... 2 \n", + "1382 2012-09-03 87 19 ... 0 \n", + "1383 2014-01-12 49 5 ... 0 \n", + "1386 2014-03-02 17 25 ... 0 \n", + "2059 2013-05-18 52 375 ... 10 \n", + "2061 2013-05-31 82 23 ... 0 \n", + "2078 2013-03-03 82 71 ... 1 \n", + "2079 2013-06-23 83 161 ... 1 \n", + "2081 2013-10-18 95 264 ... 1 \n", + "2084 2013-10-30 75 532 ... 5 \n", + "2228 2012-08-12 53 32 ... 0 \n", + "2233 2013-06-02 23 9 ... 1 \n", + "\n", + " NumStorePurchases NumWebVisitsMonth AcceptedCmp3 AcceptedCmp4 \\\n", + "10 2 7 0 0 \n", + "27 0 1 0 0 \n", + "43 4 2 0 0 \n", + "48 4 6 0 0 \n", + "58 3 6 0 0 \n", + "71 3 8 0 0 \n", + "90 8 9 0 0 \n", + "91 2 7 0 0 \n", + "92 8 1 0 0 \n", + "128 7 6 0 0 \n", + "133 7 4 0 0 \n", + "312 12 3 0 1 \n", + "319 10 7 0 1 \n", + "1379 6 5 0 0 \n", + "1382 3 5 0 0 \n", + "1383 2 7 0 0 \n", + "1386 3 7 0 0 \n", + "2059 4 3 0 0 \n", + "2061 3 6 0 0 \n", + "2078 3 8 0 0 \n", + "2079 4 6 0 0 \n", + "2081 5 7 0 0 \n", + "2084 11 1 0 0 \n", + "2228 1 0 0 1 \n", + "2233 3 6 0 0 \n", + "\n", + " AcceptedCmp5 AcceptedCmp1 AcceptedCmp2 Complain Response \n", + "10 0 0 0 0 0 \n", + "27 0 0 0 0 0 \n", + "43 0 0 0 0 0 \n", + "48 0 0 0 0 0 \n", + "58 0 0 0 0 0 \n", + "71 0 0 0 0 0 \n", + "90 0 0 0 0 0 \n", + "91 0 0 0 0 0 \n", + "92 0 0 0 0 0 \n", + "128 0 0 0 0 0 \n", + "133 0 0 0 0 0 \n", + "312 0 1 0 0 0 \n", + "319 0 1 0 0 0 \n", + "1379 0 0 0 0 0 \n", + "1382 0 0 0 0 0 \n", + "1383 0 0 0 0 0 \n", + "1386 0 0 0 0 0 \n", + "2059 0 0 0 0 0 \n", + "2061 0 0 0 0 0 \n", + "2078 0 0 0 0 0 \n", + "2079 0 0 0 0 0 \n", + "2081 0 0 0 0 0 \n", + "2084 1 0 0 0 1 \n", + "2228 0 0 0 0 0 \n", + "2233 0 0 0 0 0 \n", + "\n", + "[25 rows x 27 columns]" + ], + "text/html": [ + "\n", + "
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IDYear_BirthEducationMarital_StatusIncomeKidhomeTeenhomeDt_CustomerRecencyMntWines...NumCatalogPurchasesNumStorePurchasesNumWebVisitsMonthAcceptedCmp3AcceptedCmp4AcceptedCmp5AcceptedCmp1AcceptedCmp2ComplainResponse
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2752551986.0GraduationSingleNaN102013-02-20195...0010000000
4372811959.0PhDSingleNaN002013-11-058081...3420000000
4872441951.0GraduationSingleNaN212014-01-019648...1460000000
5885571982.0GraduationSingleNaN102013-06-175711...0360000000
71106291973.0MasterMarriedNaN102012-09-142525...0380000000
9089961957.0PhDMarriedNaN212012-11-194230...2890000000
9192351957.0GraduationSingleNaN112014-05-27457...0270000000
9257981973.0MasterTogetherNaN002013-11-2387445...4810000000
12882681961.0PhDMarriedNaN012013-07-1123352...1760000000
13312951963.0GraduationMarriedNaN012013-08-1196231...5740000000
31224371989.0GraduationMarriedNaN002013-06-0369861...51230101000
31928631970.0GraduationSingleNaN122013-08-2367738...31070101000
1379104751970.0MasterTogetherNaN012013-04-0139187...2650000000
138229021958.0GraduationTogetherNaN112012-09-038719...0350000000
138343451964.0MasterSingleNaN112014-01-12495...0270000000
138637691972.0PhDTogetherNaN102014-03-021725...0370000000
205971871969.0MasterTogetherNaN112013-05-1852375...10430000000
206116121981.0PhDSingleNaN102013-05-318223...0360000000
207850791971.0GraduationMarriedNaN112013-03-038271...1380000000
2079103391954.0MasterTogetherNaN012013-06-2383161...1460000000
208131171955.0GraduationSingleNaN012013-10-1895264...1570000000
208452501943.0MasterWidowNaN002013-10-3075532...51110010001
222887201978.0MasterTogetherNaN002012-08-125332...0100100000
223394321977.0GraduationTogetherNaN102013-06-02239...1360000000
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IDYear_BirthEducationMarital_StatusIncomeKidhomeTeenhomeDt_CustomerRecencyMntWines...ResponseEc_DivorcedEc_MarriedEc_SingleEc_TogetherEc_WidowEd_BasicEd_GraduationEd_MasterEd_PhD
055241957.0GraduationSingle58138.0002012-09-0458635...1001000100
121741954.0GraduationSingle46344.0112014-03-083811...0001000100
241411965.0GraduationTogether71613.0002013-08-2126426...0000100100
361821984.0GraduationTogether26646.0102014-02-102611...0000100100
453241981.0PhDMarried58293.0102014-01-1994173...0010000001
..................................................................
2235108701967.0GraduationMarried61223.0012013-06-1346709...0010000100
223640011946.0PhDTogether64014.0212014-06-1056406...0000100001
223772701981.0GraduationDivorced56981.0002014-01-2591908...0100000100
223882351956.0MasterTogether69245.0012014-01-248428...0000100010
223994051954.0PhDMarried52869.0112012-10-154084...1010000001
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2233 rows × 36 columns

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NumCatalogPurchases NumStorePurchases \\\n", + "Income ... 0.70 0.63 \n", + "Ec_Married ... -0.01 0.01 \n", + "Ec_Single ... -0.01 -0.02 \n", + "Ec_Together ... 0.00 -0.01 \n", + "Ec_Widow ... 0.04 0.04 \n", + "Ed_Basic ... -0.12 -0.14 \n", + "Ed_Graduation ... 0.02 0.01 \n", + "Ed_Master ... -0.04 -0.01 \n", + "Ed_PhD ... 0.06 0.05 \n", + "Year_Birth ... -0.12 -0.14 \n", + "Kidhome ... -0.50 -0.50 \n", + "Teenhome ... -0.11 0.05 \n", + "Recency ... 0.02 -0.00 \n", + "MntWines ... 0.64 0.64 \n", + "MntFruits ... 0.49 0.46 \n", + "MntMeatProducts ... 0.72 0.48 \n", + "MntFishProducts ... 0.53 0.46 \n", + "MntSweetProducts ... 0.49 0.45 \n", + "MntGoldProds ... 0.44 0.38 \n", + "NumDealsPurchases ... -0.01 0.07 \n", + "NumWebPurchases ... 0.38 0.50 \n", + "NumCatalogPurchases ... 1.00 0.52 \n", + "NumStorePurchases ... 0.52 1.00 \n", + "NumWebVisitsMonth ... -0.52 -0.43 \n", + "AcceptedCmp3 ... 0.11 -0.06 \n", + "AcceptedCmp4 ... 0.14 0.18 \n", + "AcceptedCmp5 ... 0.32 0.21 \n", + "AcceptedCmp1 ... 0.31 0.18 \n", + "AcceptedCmp2 ... 0.10 0.09 \n", + "Complain ... -0.02 -0.02 \n", + "Response ... 0.22 0.04 \n", + "\n", + " NumWebVisitsMonth AcceptedCmp3 AcceptedCmp4 \\\n", + "Income -0.65 -0.01 0.22 \n", + "Ec_Married 0.02 0.00 -0.01 \n", + "Ec_Single -0.01 0.01 -0.01 \n", + "Ec_Together -0.01 -0.02 -0.00 \n", + "Ec_Widow -0.03 -0.01 0.04 \n", + "Ed_Basic 0.10 0.02 -0.04 \n", + "Ed_Graduation -0.01 -0.01 -0.01 \n", + "Ed_Master -0.01 -0.01 -0.01 \n", + "Ed_PhD -0.01 0.01 0.04 \n", + "Year_Birth 0.12 0.06 -0.06 \n", + "Kidhome 0.45 0.01 -0.16 \n", + "Teenhome 0.13 -0.05 0.04 \n", + "Recency -0.02 -0.03 0.02 \n", + "MntWines -0.32 0.07 0.37 \n", + "MntFruits -0.42 0.02 0.01 \n", + "MntMeatProducts -0.54 0.02 0.10 \n", + "MntFishProducts -0.45 0.00 0.02 \n", + "MntSweetProducts -0.42 0.00 0.03 \n", + "MntGoldProds -0.25 0.13 0.02 \n", + "NumDealsPurchases 0.35 -0.02 0.02 \n", + "NumWebPurchases -0.06 0.05 0.16 \n", + "NumCatalogPurchases -0.52 0.11 0.14 \n", + "NumStorePurchases -0.43 -0.06 0.18 \n", + "NumWebVisitsMonth 1.00 0.06 -0.03 \n", + "AcceptedCmp3 0.06 1.00 -0.08 \n", + "AcceptedCmp4 -0.03 -0.08 1.00 \n", + "AcceptedCmp5 -0.28 0.08 0.31 \n", + "AcceptedCmp1 -0.19 0.10 0.25 \n", + "AcceptedCmp2 -0.01 0.07 0.29 \n", + "Complain 0.02 0.01 -0.03 \n", + "Response -0.00 0.25 0.18 \n", + "\n", + " AcceptedCmp5 AcceptedCmp1 AcceptedCmp2 Complain \\\n", + "Income 0.40 0.33 0.10 -0.03 \n", + "Ec_Married 0.01 0.03 -0.04 -0.00 \n", + "Ec_Single -0.01 0.00 -0.01 0.02 \n", + "Ec_Together 0.01 -0.02 0.04 -0.00 \n", + "Ec_Widow 0.02 0.00 -0.00 -0.02 \n", + "Ed_Basic -0.04 -0.04 -0.02 -0.02 \n", + "Ed_Graduation 0.01 0.03 0.01 0.03 \n", + "Ed_Master -0.01 -0.02 -0.03 0.01 \n", + "Ed_PhD 0.02 -0.00 0.03 -0.04 \n", + "Year_Birth 0.02 -0.01 -0.01 -0.00 \n", + "Kidhome -0.21 -0.17 -0.08 0.04 \n", + "Teenhome -0.19 -0.14 -0.02 0.00 \n", + "Recency -0.00 -0.02 -0.00 0.01 \n", + "MntWines 0.47 0.35 0.21 -0.04 \n", + "MntFruits 0.22 0.19 -0.01 -0.01 \n", + "MntMeatProducts 0.37 0.31 0.04 -0.02 \n", + "MntFishProducts 0.20 0.26 0.00 -0.02 \n", + "MntSweetProducts 0.26 0.24 0.01 -0.02 \n", + "MntGoldProds 0.18 0.17 0.05 -0.03 \n", + "NumDealsPurchases -0.18 -0.12 -0.04 0.00 \n", + "NumWebPurchases 0.14 0.16 0.03 -0.02 \n", + "NumCatalogPurchases 0.32 0.31 0.10 -0.02 \n", + "NumStorePurchases 0.21 0.18 0.09 -0.02 \n", + "NumWebVisitsMonth -0.28 -0.19 -0.01 0.02 \n", + "AcceptedCmp3 0.08 0.10 0.07 0.01 \n", + "AcceptedCmp4 0.31 0.25 0.29 -0.03 \n", + "AcceptedCmp5 1.00 0.40 0.22 -0.01 \n", + "AcceptedCmp1 0.40 1.00 0.18 -0.03 \n", + "AcceptedCmp2 0.22 0.18 1.00 -0.01 \n", + "Complain -0.01 -0.03 -0.01 1.00 \n", + "Response 0.33 0.30 0.17 -0.00 \n", + "\n", + " Response \n", + "Income 0.16 \n", + "Ec_Married -0.08 \n", + "Ec_Single 0.11 \n", + "Ec_Together -0.07 \n", + "Ec_Widow 0.05 \n", + "Ed_Basic -0.05 \n", + "Ed_Graduation -0.04 \n", + "Ed_Master -0.02 \n", + "Ed_PhD 0.08 \n", + "Year_Birth 0.02 \n", + "Kidhome -0.08 \n", + "Teenhome -0.16 \n", + "Recency -0.20 \n", + "MntWines 0.25 \n", + "MntFruits 0.13 \n", + "MntMeatProducts 0.24 \n", + "MntFishProducts 0.11 \n", + "MntSweetProducts 0.12 \n", + "MntGoldProds 0.14 \n", + "NumDealsPurchases 0.00 \n", + "NumWebPurchases 0.15 \n", + "NumCatalogPurchases 0.22 \n", + "NumStorePurchases 0.04 \n", + "NumWebVisitsMonth -0.00 \n", + "AcceptedCmp3 0.25 \n", + "AcceptedCmp4 0.18 \n", + "AcceptedCmp5 0.33 \n", + "AcceptedCmp1 0.30 \n", + "AcceptedCmp2 0.17 \n", + "Complain -0.00 \n", + "Response 1.00 \n", + "\n", + "[31 rows x 31 columns]" + ], + "text/html": [ + "\n", + "
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IncomeEc_MarriedEc_SingleEc_TogetherEc_WidowEd_BasicEd_GraduationEd_MasterEd_PhDYear_Birth...NumCatalogPurchasesNumStorePurchasesNumWebVisitsMonthAcceptedCmp3AcceptedCmp4AcceptedCmp5AcceptedCmp1AcceptedCmp2ComplainResponse
Income1.00-0.01-0.020.000.04-0.230.01-0.030.10-0.20...0.700.63-0.65-0.010.220.400.330.10-0.030.16
Ec_Married-0.011.00-0.42-0.47-0.15-0.01-0.00-0.000.010.05...-0.010.010.020.00-0.010.010.03-0.04-0.00-0.08
Ec_Single-0.02-0.421.00-0.31-0.100.050.02-0.03-0.010.13...-0.01-0.02-0.010.01-0.01-0.010.00-0.010.020.11
Ec_Together0.00-0.47-0.311.00-0.11-0.00-0.010.03-0.02-0.05...0.00-0.01-0.01-0.02-0.000.01-0.020.04-0.00-0.07
Ec_Widow0.04-0.15-0.10-0.111.00-0.01-0.02-0.020.04-0.17...0.040.04-0.03-0.010.040.020.00-0.00-0.020.05
Ed_Basic-0.23-0.010.05-0.00-0.011.00-0.16-0.09-0.080.12...-0.12-0.140.100.02-0.04-0.04-0.04-0.02-0.02-0.05
Ed_Graduation0.01-0.000.02-0.01-0.02-0.161.00-0.59-0.530.06...0.020.01-0.01-0.01-0.010.010.030.010.03-0.04
Ed_Master-0.03-0.00-0.030.03-0.02-0.09-0.591.00-0.310.00...-0.04-0.01-0.01-0.01-0.01-0.01-0.02-0.030.01-0.02
Ed_PhD0.100.01-0.01-0.020.04-0.08-0.53-0.311.00-0.12...0.060.05-0.010.010.040.02-0.000.03-0.040.08
Year_Birth-0.200.050.13-0.05-0.170.120.060.00-0.121.00...-0.12-0.140.120.06-0.060.02-0.01-0.01-0.000.02
Kidhome-0.510.020.020.01-0.070.050.000.02-0.040.23...-0.50-0.500.450.01-0.16-0.21-0.17-0.080.04-0.08
Teenhome0.040.01-0.100.030.05-0.12-0.02-0.020.09-0.36...-0.110.050.13-0.050.04-0.19-0.14-0.020.00-0.16
Recency0.01-0.020.010.02-0.00-0.000.03-0.03-0.01-0.02...0.02-0.00-0.02-0.030.02-0.00-0.02-0.000.01-0.20
MntWines0.69-0.01-0.020.000.04-0.14-0.06-0.030.16-0.16...0.640.64-0.320.070.370.470.350.21-0.040.25
MntFruits0.51-0.010.01-0.010.03-0.060.11-0.03-0.08-0.01...0.490.46-0.420.020.010.220.19-0.01-0.010.13
MntMeatProducts0.69-0.020.030.000.02-0.110.06-0.030.01-0.03...0.720.48-0.540.020.100.370.310.04-0.020.24
MntFishProducts0.52-0.030.010.020.05-0.060.10-0.00-0.10-0.04...0.530.46-0.450.000.020.200.260.00-0.020.11
MntSweetProducts0.52-0.010.00-0.010.05-0.060.10-0.02-0.09-0.02...0.490.45-0.420.000.030.260.240.01-0.020.12
MntGoldProds0.39-0.020.00-0.010.05-0.060.13-0.02-0.12-0.06...0.440.38-0.250.130.020.180.170.05-0.030.14
NumDealsPurchases-0.110.03-0.050.000.00-0.04-0.010.010.01-0.07...-0.010.070.35-0.020.02-0.18-0.12-0.040.000.00
NumWebPurchases0.460.00-0.04-0.000.04-0.120.01-0.030.07-0.15...0.380.50-0.060.050.160.140.160.03-0.020.15
NumCatalogPurchases0.70-0.01-0.010.000.04-0.120.02-0.040.06-0.12...1.000.52-0.520.110.140.320.310.10-0.020.22
NumStorePurchases0.630.01-0.02-0.010.04-0.140.01-0.010.05-0.14...0.521.00-0.43-0.060.180.210.180.09-0.020.04
NumWebVisitsMonth-0.650.02-0.01-0.01-0.030.10-0.01-0.01-0.010.12...-0.52-0.431.000.06-0.03-0.28-0.19-0.010.02-0.00
AcceptedCmp3-0.010.000.01-0.02-0.010.02-0.01-0.010.010.06...0.11-0.060.061.00-0.080.080.100.070.010.25
AcceptedCmp40.22-0.01-0.01-0.000.04-0.04-0.01-0.010.04-0.06...0.140.18-0.03-0.081.000.310.250.29-0.030.18
AcceptedCmp50.400.01-0.010.010.02-0.040.01-0.010.020.02...0.320.21-0.280.080.311.000.400.22-0.010.33
AcceptedCmp10.330.030.00-0.020.00-0.040.03-0.02-0.00-0.01...0.310.18-0.190.100.250.401.000.18-0.030.30
AcceptedCmp20.10-0.04-0.010.04-0.00-0.020.01-0.030.03-0.01...0.100.09-0.010.070.290.220.181.00-0.010.17
Complain-0.03-0.000.02-0.00-0.02-0.020.030.01-0.04-0.00...-0.02-0.020.020.01-0.03-0.01-0.03-0.011.00-0.00
Response0.16-0.080.11-0.070.05-0.05-0.04-0.020.080.02...0.220.04-0.000.250.180.330.300.17-0.001.00
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IDYear_BirthEducationMarital_StatusIncomeKidhomeTeenhomeDt_CustomerRecencyMntWines...AcceptedCmp5AcceptedCmp1AcceptedCmp2ComplainResponseEc_DivorcedEc_MarriedEc_SingleEc_TogetherEc_Widow
055241957.01Single58138.0002012-09-0458635...0000100100
121741954.01Single46344.0112014-03-083811...0000000100
241411965.01Together71613.0002013-08-2126426...0000000010
361821984.01Together26646.0102014-02-102611...0000000010
453241981.03Married58293.0102014-01-1994173...0000001000
..................................................................
2235108701967.01Married61223.0012013-06-1346709...0000001000
223640011946.03Together64014.0212014-06-1056406...0100000010
223772701981.01Divorced56981.0002014-01-2591908...0000010000
223882351956.02Together69245.0012014-01-248428...0000000010
223994051954.03Married52869.0112012-10-154084...0000101000
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2233 rows × 32 columns

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AcceptedCmp4 AcceptedCmp5 \\\n", + "Income 0.52 ... 0.22 0.40 \n", + "Education -0.09 ... 0.04 0.02 \n", + "Year_Birth -0.04 ... -0.06 0.02 \n", + "Kidhome -0.39 ... -0.16 -0.21 \n", + "Teenhome -0.20 ... 0.04 -0.19 \n", + "Recency -0.00 ... 0.02 -0.00 \n", + "MntWines 0.40 ... 0.37 0.47 \n", + "MntFruits 0.59 ... 0.01 0.22 \n", + "MntMeatProducts 0.57 ... 0.10 0.37 \n", + "MntFishProducts 1.00 ... 0.02 0.20 \n", + "MntSweetProducts 0.58 ... 0.03 0.26 \n", + "MntGoldProds 0.42 ... 0.02 0.18 \n", + "NumDealsPurchases -0.14 ... 0.02 -0.18 \n", + "NumWebPurchases 0.29 ... 0.16 0.14 \n", + "NumCatalogPurchases 0.53 ... 0.14 0.32 \n", + "NumStorePurchases 0.46 ... 0.18 0.21 \n", + "NumWebVisitsMonth -0.45 ... -0.03 -0.28 \n", + "AcceptedCmp3 0.00 ... -0.08 0.08 \n", + "AcceptedCmp4 0.02 ... 1.00 0.31 \n", + "AcceptedCmp5 0.20 ... 0.31 1.00 \n", + "AcceptedCmp1 0.26 ... 0.25 0.40 \n", + "AcceptedCmp2 0.00 ... 0.29 0.22 \n", + "Complain -0.02 ... -0.03 -0.01 \n", + "Response 0.11 ... 0.18 0.33 \n", + "Ec_Married -0.03 ... -0.01 0.01 \n", + "Ec_Single 0.01 ... -0.01 -0.01 \n", + "Ec_Together 0.02 ... -0.00 0.01 \n", + "Ec_Widow 0.05 ... 0.04 0.02 \n", + "\n", + " AcceptedCmp1 AcceptedCmp2 Complain Response \\\n", + "Income 0.33 0.10 -0.03 0.16 \n", + "Education -0.01 0.02 -0.03 0.08 \n", + "Year_Birth -0.01 -0.01 -0.00 0.02 \n", + "Kidhome -0.17 -0.08 0.04 -0.08 \n", + "Teenhome -0.14 -0.02 0.00 -0.16 \n", + "Recency -0.02 -0.00 0.01 -0.20 \n", + "MntWines 0.35 0.21 -0.04 0.25 \n", + "MntFruits 0.19 -0.01 -0.01 0.13 \n", + "MntMeatProducts 0.31 0.04 -0.02 0.24 \n", + "MntFishProducts 0.26 0.00 -0.02 0.11 \n", + "MntSweetProducts 0.24 0.01 -0.02 0.12 \n", + "MntGoldProds 0.17 0.05 -0.03 0.14 \n", + "NumDealsPurchases -0.12 -0.04 0.00 0.00 \n", + "NumWebPurchases 0.16 0.03 -0.02 0.15 \n", + "NumCatalogPurchases 0.31 0.10 -0.02 0.22 \n", + "NumStorePurchases 0.18 0.09 -0.02 0.04 \n", + "NumWebVisitsMonth -0.19 -0.01 0.02 -0.00 \n", + "AcceptedCmp3 0.10 0.07 0.01 0.25 \n", + "AcceptedCmp4 0.25 0.29 -0.03 0.18 \n", + "AcceptedCmp5 0.40 0.22 -0.01 0.33 \n", + "AcceptedCmp1 1.00 0.18 -0.03 0.30 \n", + "AcceptedCmp2 0.18 1.00 -0.01 0.17 \n", + "Complain -0.03 -0.01 1.00 -0.00 \n", + "Response 0.30 0.17 -0.00 1.00 \n", + "Ec_Married 0.03 -0.04 -0.00 -0.08 \n", + "Ec_Single 0.00 -0.01 0.02 0.11 \n", + "Ec_Together -0.02 0.04 -0.00 -0.07 \n", + "Ec_Widow 0.00 -0.00 -0.02 0.05 \n", + "\n", + " Ec_Married Ec_Single Ec_Together Ec_Widow \n", + "Income -0.01 -0.02 0.00 0.04 \n", + "Education 0.01 -0.04 -0.00 0.04 \n", + "Year_Birth 0.05 0.13 -0.05 -0.17 \n", + "Kidhome 0.02 0.02 0.01 -0.07 \n", + "Teenhome 0.01 -0.10 0.03 0.05 \n", + "Recency -0.02 0.01 0.02 -0.00 \n", + "MntWines -0.01 -0.02 0.00 0.04 \n", + "MntFruits -0.01 0.01 -0.01 0.03 \n", + "MntMeatProducts -0.02 0.03 0.00 0.02 \n", + "MntFishProducts -0.03 0.01 0.02 0.05 \n", + "MntSweetProducts -0.01 0.00 -0.01 0.05 \n", + "MntGoldProds -0.02 0.00 -0.01 0.05 \n", + "NumDealsPurchases 0.03 -0.05 0.00 0.00 \n", + "NumWebPurchases 0.00 -0.04 -0.00 0.04 \n", + "NumCatalogPurchases -0.01 -0.01 0.00 0.04 \n", + "NumStorePurchases 0.01 -0.02 -0.01 0.04 \n", + "NumWebVisitsMonth 0.02 -0.01 -0.01 -0.03 \n", + "AcceptedCmp3 0.00 0.01 -0.02 -0.01 \n", + "AcceptedCmp4 -0.01 -0.01 -0.00 0.04 \n", + "AcceptedCmp5 0.01 -0.01 0.01 0.02 \n", + "AcceptedCmp1 0.03 0.00 -0.02 0.00 \n", + "AcceptedCmp2 -0.04 -0.01 0.04 -0.00 \n", + "Complain -0.00 0.02 -0.00 -0.02 \n", + "Response -0.08 0.11 -0.07 0.05 \n", + "Ec_Married 1.00 -0.42 -0.47 -0.15 \n", + "Ec_Single -0.42 1.00 -0.31 -0.10 \n", + "Ec_Together -0.47 -0.31 1.00 -0.11 \n", + "Ec_Widow -0.15 -0.10 -0.11 1.00 \n", + "\n", + "[28 rows x 28 columns]" + ], + "text/html": [ + "\n", + "
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IncomeEducationYear_BirthKidhomeTeenhomeRecencyMntWinesMntFruitsMntMeatProductsMntFishProducts...AcceptedCmp4AcceptedCmp5AcceptedCmp1AcceptedCmp2ComplainResponseEc_MarriedEc_SingleEc_TogetherEc_Widow
Income1.000.13-0.20-0.510.040.010.690.510.690.52...0.220.400.330.10-0.030.16-0.01-0.020.000.04
Education0.131.00-0.14-0.040.10-0.020.16-0.080.01-0.09...0.040.02-0.010.02-0.030.080.01-0.04-0.000.04
Year_Birth-0.20-0.141.000.23-0.36-0.02-0.16-0.01-0.03-0.04...-0.060.02-0.01-0.01-0.000.020.050.13-0.05-0.17
Kidhome-0.51-0.040.231.00-0.040.01-0.50-0.37-0.44-0.39...-0.16-0.21-0.17-0.080.04-0.080.020.020.01-0.07
Teenhome0.040.10-0.36-0.041.000.020.01-0.18-0.26-0.20...0.04-0.19-0.14-0.020.00-0.160.01-0.100.030.05
Recency0.01-0.02-0.020.010.021.000.02-0.010.02-0.00...0.02-0.00-0.02-0.000.01-0.20-0.020.010.02-0.00
MntWines0.690.16-0.16-0.500.010.021.000.390.560.40...0.370.470.350.21-0.040.25-0.01-0.020.000.04
MntFruits0.51-0.08-0.01-0.37-0.18-0.010.391.000.540.59...0.010.220.19-0.01-0.010.13-0.010.01-0.010.03
MntMeatProducts0.690.01-0.03-0.44-0.260.020.560.541.000.57...0.100.370.310.04-0.020.24-0.020.030.000.02
MntFishProducts0.52-0.09-0.04-0.39-0.20-0.000.400.590.571.00...0.020.200.260.00-0.020.11-0.030.010.020.05
MntSweetProducts0.52-0.08-0.02-0.37-0.160.020.390.570.520.58...0.030.260.240.01-0.020.12-0.010.00-0.010.05
MntGoldProds0.39-0.11-0.06-0.35-0.020.020.390.390.350.42...0.020.180.170.05-0.030.14-0.020.00-0.010.05
NumDealsPurchases-0.110.03-0.070.220.390.000.01-0.13-0.12-0.14...0.02-0.18-0.12-0.040.000.000.03-0.050.000.00
NumWebPurchases0.460.07-0.15-0.360.16-0.010.540.300.290.29...0.160.140.160.03-0.020.150.00-0.04-0.000.04
NumCatalogPurchases0.700.06-0.12-0.50-0.110.020.640.490.720.53...0.140.320.310.10-0.020.22-0.01-0.010.000.04
NumStorePurchases0.630.07-0.14-0.500.05-0.000.640.460.480.46...0.180.210.180.09-0.020.040.01-0.02-0.010.04
NumWebVisitsMonth-0.65-0.040.120.450.13-0.02-0.32-0.42-0.54-0.45...-0.03-0.28-0.19-0.010.02-0.000.02-0.01-0.01-0.03
AcceptedCmp3-0.010.000.060.01-0.05-0.030.070.020.020.00...-0.080.080.100.070.010.250.000.01-0.02-0.01
AcceptedCmp40.220.04-0.06-0.160.040.020.370.010.100.02...1.000.310.250.29-0.030.18-0.01-0.01-0.000.04
AcceptedCmp50.400.020.02-0.21-0.19-0.000.470.220.370.20...0.311.000.400.22-0.010.330.01-0.010.010.02
AcceptedCmp10.33-0.01-0.01-0.17-0.14-0.020.350.190.310.26...0.250.401.000.18-0.030.300.030.00-0.020.00
AcceptedCmp20.100.02-0.01-0.08-0.02-0.000.21-0.010.040.00...0.290.220.181.00-0.010.17-0.04-0.010.04-0.00
Complain-0.03-0.03-0.000.040.000.01-0.04-0.01-0.02-0.02...-0.03-0.01-0.03-0.011.00-0.00-0.000.02-0.00-0.02
Response0.160.080.02-0.08-0.16-0.200.250.130.240.11...0.180.330.300.17-0.001.00-0.080.11-0.070.05
Ec_Married-0.010.010.050.020.01-0.02-0.01-0.01-0.02-0.03...-0.010.010.03-0.04-0.00-0.081.00-0.42-0.47-0.15
Ec_Single-0.02-0.040.130.02-0.100.01-0.020.010.030.01...-0.01-0.010.00-0.010.020.11-0.421.00-0.31-0.10
Ec_Together0.00-0.00-0.050.010.030.020.00-0.010.000.02...-0.000.01-0.020.04-0.00-0.07-0.47-0.311.00-0.11
Ec_Widow0.040.04-0.17-0.070.05-0.000.040.030.020.05...0.040.020.00-0.00-0.020.05-0.15-0.10-0.111.00
\n", + "

28 rows × 28 columns

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\n", + " " + ] + }, + "metadata": {}, + "execution_count": 97 + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "Link de material explicativo sobre essa metodologia: [Categorical Encoding](https://pbpython.com/categorical-encoding.html)" + ], + "metadata": { + "id": "_YA6pE2GPCjg" + } + }, + { + "cell_type": "code", + "source": [ + "## Bom, fato é que em ambos os casos não obtivemos resultados muito expressivos sobre uma alta correlação entre o EC, a ED e a renda.\n", + "## Mas dadas as opções, prefiro trabalhar com as variáveis dummies da segunda tabela. Nela, temos a educação \"basic\" com uma correlação mais forte do que a var. \"education\" da tabela 3.\n", + "## Assim, vamos seguir a análise.\n", + "df_2[['Income', 'Ec_Married', 'Ec_Single',\n", + " 'Ec_Together', 'Ec_Widow',\t'Ed_Basic',\t'Ed_Graduation',\t\"Ed_Master\",\t'Ed_PhD', \n", + " 'Year_Birth', 'Kidhome','Teenhome', 'Recency', 'MntWines', 'MntFruits',\n", + " 'MntMeatProducts', 'MntFishProducts', 'MntSweetProducts',\n", + " 'MntGoldProds', 'NumDealsPurchases', 'NumWebPurchases',\n", + " 'NumCatalogPurchases', 'NumStorePurchases', 'NumWebVisitsMonth',\n", + " 'AcceptedCmp3', 'AcceptedCmp4', 'AcceptedCmp5', 'AcceptedCmp1',\n", + " 'AcceptedCmp2', 'Complain', 'Response']].corr(method='pearson').round(2)" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000 + }, + "id": "Utg6lDJ8pBT6", + "outputId": "fe99304e-d1b2-4702-a43a-f1ce6c26ec7d" + }, + "execution_count": 98, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + " Income Ec_Married Ec_Single Ec_Together Ec_Widow \\\n", + "Income 1.00 -0.01 -0.02 0.00 0.04 \n", + "Ec_Married -0.01 1.00 -0.42 -0.47 -0.15 \n", + "Ec_Single -0.02 -0.42 1.00 -0.31 -0.10 \n", + "Ec_Together 0.00 -0.47 -0.31 1.00 -0.11 \n", + "Ec_Widow 0.04 -0.15 -0.10 -0.11 1.00 \n", + "Ed_Basic -0.23 -0.01 0.05 -0.00 -0.01 \n", + "Ed_Graduation 0.01 -0.00 0.02 -0.01 -0.02 \n", + "Ed_Master -0.03 -0.00 -0.03 0.03 -0.02 \n", + "Ed_PhD 0.10 0.01 -0.01 -0.02 0.04 \n", + "Year_Birth -0.20 0.05 0.13 -0.05 -0.17 \n", + "Kidhome -0.51 0.02 0.02 0.01 -0.07 \n", + "Teenhome 0.04 0.01 -0.10 0.03 0.05 \n", + "Recency 0.01 -0.02 0.01 0.02 -0.00 \n", + "MntWines 0.69 -0.01 -0.02 0.00 0.04 \n", + "MntFruits 0.51 -0.01 0.01 -0.01 0.03 \n", + "MntMeatProducts 0.69 -0.02 0.03 0.00 0.02 \n", + "MntFishProducts 0.52 -0.03 0.01 0.02 0.05 \n", + "MntSweetProducts 0.52 -0.01 0.00 -0.01 0.05 \n", + "MntGoldProds 0.39 -0.02 0.00 -0.01 0.05 \n", + "NumDealsPurchases -0.11 0.03 -0.05 0.00 0.00 \n", + "NumWebPurchases 0.46 0.00 -0.04 -0.00 0.04 \n", + "NumCatalogPurchases 0.70 -0.01 -0.01 0.00 0.04 \n", + "NumStorePurchases 0.63 0.01 -0.02 -0.01 0.04 \n", + "NumWebVisitsMonth -0.65 0.02 -0.01 -0.01 -0.03 \n", + "AcceptedCmp3 -0.01 0.00 0.01 -0.02 -0.01 \n", + "AcceptedCmp4 0.22 -0.01 -0.01 -0.00 0.04 \n", + "AcceptedCmp5 0.40 0.01 -0.01 0.01 0.02 \n", + "AcceptedCmp1 0.33 0.03 0.00 -0.02 0.00 \n", + "AcceptedCmp2 0.10 -0.04 -0.01 0.04 -0.00 \n", + "Complain -0.03 -0.00 0.02 -0.00 -0.02 \n", + "Response 0.16 -0.08 0.11 -0.07 0.05 \n", + "\n", + " Ed_Basic Ed_Graduation Ed_Master Ed_PhD Year_Birth \\\n", + "Income -0.23 0.01 -0.03 0.10 -0.20 \n", + "Ec_Married -0.01 -0.00 -0.00 0.01 0.05 \n", + "Ec_Single 0.05 0.02 -0.03 -0.01 0.13 \n", + "Ec_Together -0.00 -0.01 0.03 -0.02 -0.05 \n", + "Ec_Widow -0.01 -0.02 -0.02 0.04 -0.17 \n", + "Ed_Basic 1.00 -0.16 -0.09 -0.08 0.12 \n", + "Ed_Graduation -0.16 1.00 -0.59 -0.53 0.06 \n", + "Ed_Master -0.09 -0.59 1.00 -0.31 0.00 \n", + "Ed_PhD -0.08 -0.53 -0.31 1.00 -0.12 \n", + "Year_Birth 0.12 0.06 0.00 -0.12 1.00 \n", + "Kidhome 0.05 0.00 0.02 -0.04 0.23 \n", + "Teenhome -0.12 -0.02 -0.02 0.09 -0.36 \n", + "Recency -0.00 0.03 -0.03 -0.01 -0.02 \n", + "MntWines -0.14 -0.06 -0.03 0.16 -0.16 \n", + "MntFruits -0.06 0.11 -0.03 -0.08 -0.01 \n", + "MntMeatProducts -0.11 0.06 -0.03 0.01 -0.03 \n", + "MntFishProducts -0.06 0.10 -0.00 -0.10 -0.04 \n", + "MntSweetProducts -0.06 0.10 -0.02 -0.09 -0.02 \n", + "MntGoldProds -0.06 0.13 -0.02 -0.12 -0.06 \n", + "NumDealsPurchases -0.04 -0.01 0.01 0.01 -0.07 \n", + "NumWebPurchases -0.12 0.01 -0.03 0.07 -0.15 \n", + "NumCatalogPurchases -0.12 0.02 -0.04 0.06 -0.12 \n", + "NumStorePurchases -0.14 0.01 -0.01 0.05 -0.14 \n", + "NumWebVisitsMonth 0.10 -0.01 -0.01 -0.01 0.12 \n", + "AcceptedCmp3 0.02 -0.01 -0.01 0.01 0.06 \n", + "AcceptedCmp4 -0.04 -0.01 -0.01 0.04 -0.06 \n", + "AcceptedCmp5 -0.04 0.01 -0.01 0.02 0.02 \n", + "AcceptedCmp1 -0.04 0.03 -0.02 -0.00 -0.01 \n", + "AcceptedCmp2 -0.02 0.01 -0.03 0.03 -0.01 \n", + "Complain -0.02 0.03 0.01 -0.04 -0.00 \n", + "Response -0.05 -0.04 -0.02 0.08 0.02 \n", + "\n", + " ... NumCatalogPurchases NumStorePurchases \\\n", + "Income ... 0.70 0.63 \n", + "Ec_Married ... -0.01 0.01 \n", + "Ec_Single ... -0.01 -0.02 \n", + "Ec_Together ... 0.00 -0.01 \n", + "Ec_Widow ... 0.04 0.04 \n", + "Ed_Basic ... -0.12 -0.14 \n", + "Ed_Graduation ... 0.02 0.01 \n", + "Ed_Master ... -0.04 -0.01 \n", + "Ed_PhD ... 0.06 0.05 \n", + "Year_Birth ... -0.12 -0.14 \n", + "Kidhome ... -0.50 -0.50 \n", + "Teenhome ... -0.11 0.05 \n", + "Recency ... 0.02 -0.00 \n", + "MntWines ... 0.64 0.64 \n", + "MntFruits ... 0.49 0.46 \n", + "MntMeatProducts ... 0.72 0.48 \n", + "MntFishProducts ... 0.53 0.46 \n", + "MntSweetProducts ... 0.49 0.45 \n", + "MntGoldProds ... 0.44 0.38 \n", + "NumDealsPurchases ... -0.01 0.07 \n", + "NumWebPurchases ... 0.38 0.50 \n", + "NumCatalogPurchases ... 1.00 0.52 \n", + "NumStorePurchases ... 0.52 1.00 \n", + "NumWebVisitsMonth ... -0.52 -0.43 \n", + "AcceptedCmp3 ... 0.11 -0.06 \n", + "AcceptedCmp4 ... 0.14 0.18 \n", + "AcceptedCmp5 ... 0.32 0.21 \n", + "AcceptedCmp1 ... 0.31 0.18 \n", + "AcceptedCmp2 ... 0.10 0.09 \n", + "Complain ... -0.02 -0.02 \n", + "Response ... 0.22 0.04 \n", + "\n", + " NumWebVisitsMonth AcceptedCmp3 AcceptedCmp4 \\\n", + "Income -0.65 -0.01 0.22 \n", + "Ec_Married 0.02 0.00 -0.01 \n", + "Ec_Single -0.01 0.01 -0.01 \n", + "Ec_Together -0.01 -0.02 -0.00 \n", + "Ec_Widow -0.03 -0.01 0.04 \n", + "Ed_Basic 0.10 0.02 -0.04 \n", + "Ed_Graduation -0.01 -0.01 -0.01 \n", + "Ed_Master -0.01 -0.01 -0.01 \n", + "Ed_PhD -0.01 0.01 0.04 \n", + "Year_Birth 0.12 0.06 -0.06 \n", + "Kidhome 0.45 0.01 -0.16 \n", + "Teenhome 0.13 -0.05 0.04 \n", + "Recency -0.02 -0.03 0.02 \n", + "MntWines -0.32 0.07 0.37 \n", + "MntFruits -0.42 0.02 0.01 \n", + "MntMeatProducts -0.54 0.02 0.10 \n", + "MntFishProducts -0.45 0.00 0.02 \n", + "MntSweetProducts -0.42 0.00 0.03 \n", + "MntGoldProds -0.25 0.13 0.02 \n", + "NumDealsPurchases 0.35 -0.02 0.02 \n", + "NumWebPurchases -0.06 0.05 0.16 \n", + "NumCatalogPurchases -0.52 0.11 0.14 \n", + "NumStorePurchases -0.43 -0.06 0.18 \n", + "NumWebVisitsMonth 1.00 0.06 -0.03 \n", + "AcceptedCmp3 0.06 1.00 -0.08 \n", + "AcceptedCmp4 -0.03 -0.08 1.00 \n", + "AcceptedCmp5 -0.28 0.08 0.31 \n", + "AcceptedCmp1 -0.19 0.10 0.25 \n", + "AcceptedCmp2 -0.01 0.07 0.29 \n", + "Complain 0.02 0.01 -0.03 \n", + "Response -0.00 0.25 0.18 \n", + "\n", + " AcceptedCmp5 AcceptedCmp1 AcceptedCmp2 Complain \\\n", + "Income 0.40 0.33 0.10 -0.03 \n", + "Ec_Married 0.01 0.03 -0.04 -0.00 \n", + "Ec_Single -0.01 0.00 -0.01 0.02 \n", + "Ec_Together 0.01 -0.02 0.04 -0.00 \n", + "Ec_Widow 0.02 0.00 -0.00 -0.02 \n", + "Ed_Basic -0.04 -0.04 -0.02 -0.02 \n", + "Ed_Graduation 0.01 0.03 0.01 0.03 \n", + "Ed_Master -0.01 -0.02 -0.03 0.01 \n", + "Ed_PhD 0.02 -0.00 0.03 -0.04 \n", + "Year_Birth 0.02 -0.01 -0.01 -0.00 \n", + "Kidhome -0.21 -0.17 -0.08 0.04 \n", + "Teenhome -0.19 -0.14 -0.02 0.00 \n", + "Recency -0.00 -0.02 -0.00 0.01 \n", + "MntWines 0.47 0.35 0.21 -0.04 \n", + "MntFruits 0.22 0.19 -0.01 -0.01 \n", + "MntMeatProducts 0.37 0.31 0.04 -0.02 \n", + "MntFishProducts 0.20 0.26 0.00 -0.02 \n", + "MntSweetProducts 0.26 0.24 0.01 -0.02 \n", + "MntGoldProds 0.18 0.17 0.05 -0.03 \n", + "NumDealsPurchases -0.18 -0.12 -0.04 0.00 \n", + "NumWebPurchases 0.14 0.16 0.03 -0.02 \n", + "NumCatalogPurchases 0.32 0.31 0.10 -0.02 \n", + "NumStorePurchases 0.21 0.18 0.09 -0.02 \n", + "NumWebVisitsMonth -0.28 -0.19 -0.01 0.02 \n", + "AcceptedCmp3 0.08 0.10 0.07 0.01 \n", + "AcceptedCmp4 0.31 0.25 0.29 -0.03 \n", + "AcceptedCmp5 1.00 0.40 0.22 -0.01 \n", + "AcceptedCmp1 0.40 1.00 0.18 -0.03 \n", + "AcceptedCmp2 0.22 0.18 1.00 -0.01 \n", + "Complain -0.01 -0.03 -0.01 1.00 \n", + "Response 0.33 0.30 0.17 -0.00 \n", + "\n", + " Response \n", + "Income 0.16 \n", + "Ec_Married -0.08 \n", + "Ec_Single 0.11 \n", + "Ec_Together -0.07 \n", + "Ec_Widow 0.05 \n", + "Ed_Basic -0.05 \n", + "Ed_Graduation -0.04 \n", + "Ed_Master -0.02 \n", + "Ed_PhD 0.08 \n", + "Year_Birth 0.02 \n", + "Kidhome -0.08 \n", + "Teenhome -0.16 \n", + "Recency -0.20 \n", + "MntWines 0.25 \n", + "MntFruits 0.13 \n", + "MntMeatProducts 0.24 \n", + "MntFishProducts 0.11 \n", + "MntSweetProducts 0.12 \n", + "MntGoldProds 0.14 \n", + "NumDealsPurchases 0.00 \n", + "NumWebPurchases 0.15 \n", + "NumCatalogPurchases 0.22 \n", + "NumStorePurchases 0.04 \n", + "NumWebVisitsMonth -0.00 \n", + "AcceptedCmp3 0.25 \n", + "AcceptedCmp4 0.18 \n", + "AcceptedCmp5 0.33 \n", + "AcceptedCmp1 0.30 \n", + "AcceptedCmp2 0.17 \n", + "Complain -0.00 \n", + "Response 1.00 \n", + "\n", + "[31 rows x 31 columns]" + ], + "text/html": [ + "\n", + "
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IncomeEc_MarriedEc_SingleEc_TogetherEc_WidowEd_BasicEd_GraduationEd_MasterEd_PhDYear_Birth...NumCatalogPurchasesNumStorePurchasesNumWebVisitsMonthAcceptedCmp3AcceptedCmp4AcceptedCmp5AcceptedCmp1AcceptedCmp2ComplainResponse
Income1.00-0.01-0.020.000.04-0.230.01-0.030.10-0.20...0.700.63-0.65-0.010.220.400.330.10-0.030.16
Ec_Married-0.011.00-0.42-0.47-0.15-0.01-0.00-0.000.010.05...-0.010.010.020.00-0.010.010.03-0.04-0.00-0.08
Ec_Single-0.02-0.421.00-0.31-0.100.050.02-0.03-0.010.13...-0.01-0.02-0.010.01-0.01-0.010.00-0.010.020.11
Ec_Together0.00-0.47-0.311.00-0.11-0.00-0.010.03-0.02-0.05...0.00-0.01-0.01-0.02-0.000.01-0.020.04-0.00-0.07
Ec_Widow0.04-0.15-0.10-0.111.00-0.01-0.02-0.020.04-0.17...0.040.04-0.03-0.010.040.020.00-0.00-0.020.05
Ed_Basic-0.23-0.010.05-0.00-0.011.00-0.16-0.09-0.080.12...-0.12-0.140.100.02-0.04-0.04-0.04-0.02-0.02-0.05
Ed_Graduation0.01-0.000.02-0.01-0.02-0.161.00-0.59-0.530.06...0.020.01-0.01-0.01-0.010.010.030.010.03-0.04
Ed_Master-0.03-0.00-0.030.03-0.02-0.09-0.591.00-0.310.00...-0.04-0.01-0.01-0.01-0.01-0.01-0.02-0.030.01-0.02
Ed_PhD0.100.01-0.01-0.020.04-0.08-0.53-0.311.00-0.12...0.060.05-0.010.010.040.02-0.000.03-0.040.08
Year_Birth-0.200.050.13-0.05-0.170.120.060.00-0.121.00...-0.12-0.140.120.06-0.060.02-0.01-0.01-0.000.02
Kidhome-0.510.020.020.01-0.070.050.000.02-0.040.23...-0.50-0.500.450.01-0.16-0.21-0.17-0.080.04-0.08
Teenhome0.040.01-0.100.030.05-0.12-0.02-0.020.09-0.36...-0.110.050.13-0.050.04-0.19-0.14-0.020.00-0.16
Recency0.01-0.020.010.02-0.00-0.000.03-0.03-0.01-0.02...0.02-0.00-0.02-0.030.02-0.00-0.02-0.000.01-0.20
MntWines0.69-0.01-0.020.000.04-0.14-0.06-0.030.16-0.16...0.640.64-0.320.070.370.470.350.21-0.040.25
MntFruits0.51-0.010.01-0.010.03-0.060.11-0.03-0.08-0.01...0.490.46-0.420.020.010.220.19-0.01-0.010.13
MntMeatProducts0.69-0.020.030.000.02-0.110.06-0.030.01-0.03...0.720.48-0.540.020.100.370.310.04-0.020.24
MntFishProducts0.52-0.030.010.020.05-0.060.10-0.00-0.10-0.04...0.530.46-0.450.000.020.200.260.00-0.020.11
MntSweetProducts0.52-0.010.00-0.010.05-0.060.10-0.02-0.09-0.02...0.490.45-0.420.000.030.260.240.01-0.020.12
MntGoldProds0.39-0.020.00-0.010.05-0.060.13-0.02-0.12-0.06...0.440.38-0.250.130.020.180.170.05-0.030.14
NumDealsPurchases-0.110.03-0.050.000.00-0.04-0.010.010.01-0.07...-0.010.070.35-0.020.02-0.18-0.12-0.040.000.00
NumWebPurchases0.460.00-0.04-0.000.04-0.120.01-0.030.07-0.15...0.380.50-0.060.050.160.140.160.03-0.020.15
NumCatalogPurchases0.70-0.01-0.010.000.04-0.120.02-0.040.06-0.12...1.000.52-0.520.110.140.320.310.10-0.020.22
NumStorePurchases0.630.01-0.02-0.010.04-0.140.01-0.010.05-0.14...0.521.00-0.43-0.060.180.210.180.09-0.020.04
NumWebVisitsMonth-0.650.02-0.01-0.01-0.030.10-0.01-0.01-0.010.12...-0.52-0.431.000.06-0.03-0.28-0.19-0.010.02-0.00
AcceptedCmp3-0.010.000.01-0.02-0.010.02-0.01-0.010.010.06...0.11-0.060.061.00-0.080.080.100.070.010.25
AcceptedCmp40.22-0.01-0.01-0.000.04-0.04-0.01-0.010.04-0.06...0.140.18-0.03-0.081.000.310.250.29-0.030.18
AcceptedCmp50.400.01-0.010.010.02-0.040.01-0.010.020.02...0.320.21-0.280.080.311.000.400.22-0.010.33
AcceptedCmp10.330.030.00-0.020.00-0.040.03-0.02-0.00-0.01...0.310.18-0.190.100.250.401.000.18-0.030.30
AcceptedCmp20.10-0.04-0.010.04-0.00-0.020.01-0.030.03-0.01...0.100.09-0.010.070.290.220.181.00-0.010.17
Complain-0.03-0.000.02-0.00-0.02-0.020.030.01-0.04-0.00...-0.02-0.020.020.01-0.03-0.01-0.03-0.011.00-0.00
Response0.16-0.080.11-0.070.05-0.05-0.04-0.020.080.02...0.220.04-0.000.250.180.330.300.17-0.001.00
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\n", + " " + ] + }, + "metadata": {}, + "execution_count": 98 + } + ] + }, + { + "cell_type": "code", + "source": [ + "## Analisando essa tabela, vou manter apenas as seguintes variáveis na tabela de correlação e vou gerar uma visualização dessa tabela para facilitar a escolha das variáveis do modelo:\n", + "cm_1=df_2[['Income', 'Kidhome',\n", + " 'MntWines', 'MntFruits',\n", + " 'MntMeatProducts', 'MntFishProducts', 'MntSweetProducts',\n", + " 'NumCatalogPurchases', 'NumStorePurchases', 'NumWebVisitsMonth'\n", + " ]].corr(method='pearson').round(2)" + ], + "metadata": { + "id": "aWwt_iqvgBK9" + }, + "execution_count": 99, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "## Criando quadro para plotar figura da correlação\n", + "\n", + "fig_1, ax_1= plt.subplots(figsize=(13,7))\n", + "\n", + "## Crianndo o título\n", + "plt.title(\"Heatmap das correlações\",fontsize=18)\n", + "ttl = ax_1.title\n", + "ttl.set_position([0.5,1.05])\n", + "\n", + "## Criando a visualização do heatmap com o Seaborn\n", + "sns.heatmap(cm_1, vmin=-1.0,vmax=1.0,annot=True,cmap='RdYlGn',linewidths=0.30,ax=ax_1)\n", + "plt.show()" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 570 + }, + "id": "T5QCyZJEnAf1", + "outputId": "e90ba8e8-36c4-4559-8d07-8ec0d5abad6a" + }, + "execution_count": 100, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
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\n" + }, + "metadata": { + "needs_background": "light" + } + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "Analisando a figura, escolhi as seguintes variáveis explicativas pro modelo:\n", + "\n", + "- 'Kidhome',\n", + "- 'MntWines',\n", + "- 'MntFruits',\n", + "- 'MntFishProducts', \n", + "- 'MntSweetProducts',\n", + "- 'NumCatalogPurchases',\n", + "- 'NumStorePurchases',\n", + "- 'NumWebVisitsMonth'.\n", + "\n", + "Todas elas tem correlação maior do que 0,5 com a renda e nenhuma delas tem correlação maior do que 0,7 com outra das variáveis explicativas do modelo.\n", + "\n" + ], + "metadata": { + "id": "SGH8xLDrxRNh" + } + }, + { + "cell_type": "markdown", + "source": [ + "###### **Modelo de regressão linear:**" + ], + "metadata": { + "id": "r8vS1uKk9nwU" + } + }, + { + "cell_type": "code", + "source": [ + "## Criando dataframes do nosso Y e dos nossos Xs\n", + "\n", + "df_4 = df_2[~df_2['Income'].isnull()] ##esse comando foi preciso porque a classe lr não funciona com dados nulos, logo tive que fazer uma copia da DF que vamos utilizar, porém sem os nulos.\n", + "X = df_4[['Kidhome',\n", + "'MntWines',\n", + "'MntFruits',\n", + "'MntFishProducts', \n", + "'MntSweetProducts',\n", + "'NumCatalogPurchases',\n", + "'NumStorePurchases',\n", + "'NumWebVisitsMonth']]\n", + "Y = df_4[['Income']]" + ], + "metadata": { + "id": "SqhvI12Z8Lfq" + }, + "execution_count": 101, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "from sklearn.linear_model import LinearRegression" + ], + "metadata": { + "id": "Ktt7RD_f1ym3" + }, + "execution_count": 102, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "## Criando objeto da classe Linear Regression\n", + "lr = LinearRegression()" + ], + "metadata": { + "id": "zZcbdvbu8C1w" + }, + "execution_count": 103, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "from sklearn.model_selection import train_test_split" + ], + "metadata": { + "id": "r7bd4TuAAV1b" + }, + "execution_count": 104, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "X_train, X_valid, Y_train, Y_valid = train_test_split(X, Y, random_state=42, test_size=0.3) \n", + "## Vou separar para a validação/teste 30% da amostra e para treino 70%, esse método é chamado de holdout e é bem aceito no meio corporativo. \n", + "## 70% da amostra irá passar pelo FIT e os outros 30% serão utilizados para validação.\n", + "## o random state irá fixar ou não um valor para o train, teste e split começar. 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\n", + " " + ] + }, + "metadata": {}, + "execution_count": 112 + } + ] + }, + { + "cell_type": "code", + "source": [ + "from sklearn.metrics import r2_score, mean_absolute_error, mean_squared_error" + ], + "metadata": { + "id": "XBsoHbpRJYfV" + }, + "execution_count": 113, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "r2= r2_score(Y_valid, YHat)\n", + "print('As variáveis explicativas do meu modelo explicam', (r2*100).round(2), \"% das variações na renda dos clientes\")" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "VcQDedCmJUtC", + "outputId": "925b1241-6945-4810-d412-2d1da933149d" + }, + "execution_count": 114, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "As variáveis explicativas do meu modelo explicam 65.08 % das variações na renda dos clientes\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "m_abe=mean_absolute_error(Y_valid,YHat)\n", + "print('O erro médio absoluto do modelo é:', (m_abe).round(2))" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "MtbOJ6PEM-HX", + "outputId": "c2ff6901-5347-4619-c24d-f6c9855415cf" + }, + "execution_count": 115, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "O erro médio absoluto do modelo é: 8683.5\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "m_sqe=mean_squared_error(Y_valid,YHat)\n", + "print('O erro médio quadrático do modelo é:', (m_sqe).round(2))" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "kvpHmglINPQS", + "outputId": "58e71a56-df51-4a62-9aa0-3be9c4b44a93" + }, + "execution_count": 116, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "O erro médio quadrático do modelo é: 161400019.55\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "import math\n", + "m_sqe_sqrt=math.sqrt(m_sqe)\n", + "print('A raiz quadrada do erro médio quadrático é:', (m_sqe_sqrt))" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "YG8Vrlkt5lFu", + "outputId": "bf46593f-32dd-46e8-a401-0a2da0a63226" + }, + "execution_count": 117, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "A raiz quadrada do erro médio quadrático é: 12704.330739940186\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "## Vendo se os valores previstos ficaram bem ajustados\n", + "sns.distplot(YHat,hist=False,label='ValorEst')\n", + "sns.distplot(Y_valid,hist=False,color='r',label='ValorReal')\n", + "plt.show()" + ], + "metadata": { + "id": "JytAQ_6p5lkz", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 365 + }, + "outputId": "e81baf55-c102-45fc-a180-b1737136f205" + }, + "execution_count": 118, + "outputs": [ + { + "output_type": "stream", + "name": "stderr", + "text": [ + "/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `kdeplot` (an axes-level function for kernel density plots).\n", + " warnings.warn(msg, FutureWarning)\n", + "/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `kdeplot` (an axes-level function for kernel density plots).\n", + " warnings.warn(msg, FutureWarning)\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
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\n" + }, + "metadata": { + "needs_background": "light" + } + } + ] + }, + { + "cell_type": "code", + "source": [ + "## O resultado talvez pode ser melhor, vou aplicar um modelo polinomial para ver se será mais proveitoso." + ], + "metadata": { + "id": "y70w3qkTm-M5" + }, + "execution_count": 119, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "###### **Modelo de regressão linear com polinomial transformado e usando um pipeline manual:**" + ], + "metadata": { + "id": "_4WwzbFeFtR2" + } + }, + { + "cell_type": "code", + "source": [ + "from sklearn.preprocessing import PolynomialFeatures" + ], + "metadata": { + "id": "6gdKOn1OFylj" + }, + "execution_count": 120, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "## Criando as funções para o pipeline:\n", + "\n", + "def aplica_polinomial(X_train3, X_valid3,grau=2):\n", + " pf=PolynomialFeatures(degree=grau)\n", + " X_train3_pf=pf.fit_transform(X_train3)\n", + " X_valid3_pf=pf.transform(X_valid3)\n", + " return X_train3_pf, X_valid3_pf\n", + "\n", + "def aplica_modelo(X_train3, Y_train3, X_valid3):\n", + " lr=LinearRegression()\n", + " lr.fit(X_train3,Y_train3)\n", + " Yhat_valid3=lr.predict(X_valid3)\n", + " Yhat_train3=lr.predict(X_train3)\n", + " ##print('Acurácia do modelo de treino:', lr.score(X_train3, Y_train3).round(2))\n", + " return Yhat_valid3, Yhat_train3\n", + "\n", + "def evaluate_model(Yhat_valid3,Y_valid3,X_valid3):\n", + " r2_pf3= r2_score(Y_valid3,Yhat_valid3)\n", + " print('As variáveis explicativas do meu modelo explicam', (r2_pf3*100).round(2), \"% das variações na renda dos clientes.\")\n", + " m_abe_pf3=mean_absolute_error(Y_valid3,Yhat_valid3)\n", + " print('O erro médio absoluto do modelo é:', (m_abe_pf3).round(2))\n", + " m_sqe_pf3=mean_squared_error(Y_valid3,Yhat_valid3)\n", + " print('O erro médio quadrático do modelo é:', (m_sqe_pf3).round(2))\n", + " m_sqe_sqrt_pf3=math.sqrt(m_sqe)\n", + " print('A raiz quadrada do erro médio quadrático é:', (m_sqe_sqrt_pf3))\n", + " print('Acurácia:', lr.score(X_valid3, Y_valid3).round(2))\n", + " print('\\nVeja o comportamento dos resíduos:')\n", + " sns.residplot(x= Y_valid3,y= Yhat_valid3)\n", + " plt.title('Resíduos')\n", + " plt.show()\n", + "\n", + "def Pipeline_Regressao(X, Y, grau=2):\n", + " X_train3, X_valid3, Y_train3, Y_valid3= train_test_split(X3,Y3,test_size=0.3,random_state=42)\n", + " X_train3_pf, X_valid3_pf = aplica_polinomial(X_train3, X_valid3, grau)\n", + " Yhat_valid3, Yhat_train3 = aplica_modelo(X_train3_pf, Y_train3, X_valid3_pf)\n", + " print('Resultados do Polinomial de Grau:', grau)\n", + " print('\\nResultado do conjunto de treino ','- Grau',grau,':')\n", + " evaluate_model(Yhat_train3, Y_train3, X_train3)\n", + " print('\\nResultado do conjunto de teste ','- Grau',grau,':')\n", + " evaluate_model(Yhat_valid3, Y_valid3, X_valid3)\n", + " print('---------------------------\\n')\n", + " \n" + ], + "metadata": { + "id": "WnkdjQOrR6W1" + }, + "execution_count": 121, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "X3 = df_4[['Kidhome',\n", + "'MntWines',\n", + "'MntFruits',\n", + "'MntFishProducts', \n", + "'MntSweetProducts',\n", + "'NumCatalogPurchases',\n", + "'NumStorePurchases',\n", + "'NumWebVisitsMonth']]\n", + "Y3 = df_4[['Income']]" + ], + "metadata": { + "id": "HYu_l07099lc" + }, + "execution_count": 122, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "for i in range(1, 10):\n", + " Pipeline_Regressao(X3, Y3, i)" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000 + }, + "id": "L7it4IdOX9dL", + "outputId": "bb960074-cc30-4f37-a95f-95907e2a31d6" + }, + "execution_count": 123, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Resultados do Polinomial de Grau: 1\n", + "\n", + "Resultado do conjunto de treino - Grau 1 :\n", + "As variáveis explicativas do meu modelo explicam 74.66 % das variações na renda dos clientes.\n", + "O erro médio absoluto do modelo é: 7988.58\n", + "O erro médio quadrático do modelo é: 117803313.01\n", + "A raiz quadrada do erro médio quadrático é: 12704.330739940186\n", + "Acurácia: 0.75\n", + "\n", + "Veja o comportamento dos resíduos:\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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\n" + }, + "metadata": { + "needs_background": "light" + } + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "\n", + "Resultado do conjunto de teste - Grau 1 :\n", + "As variáveis explicativas do meu modelo explicam 65.08 % das variações na renda dos clientes.\n", + "O erro médio absoluto do modelo é: 8683.5\n", + "O erro médio quadrático do modelo é: 161400023.16\n", + "A raiz quadrada do erro médio quadrático é: 12704.330739940186\n", + "Acurácia: 0.65\n", + "\n", + "Veja o comportamento dos resíduos:\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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5wL0VyPu8KTmLLVhddAVMbE84d+1SHtrbPa59obBxe3ldkuHuYRQXJMJQCVHqq+JFb8yVmP5lKm0emzes5WPbdjOczo5rs7BeU2ahVbyBW0TuBr7ofzaq6gEROQnYrqq/KiJb/Os7/PHPABtzP6q62advwS3Ruh34qaqu8+mXFh5XymJr4M41rBbebIbTWVrqq2zK8ykqdbOd2Ch8ZChF50Ca5roEy+uS+RtuTTxCJtT8v8HLR4boG80CEBFoqIrRUJ0o2phcrOG5fyTDstoEg+mAukQUEWEglS0aCIrlsXsoQ10yyhkrGsYdn/ueE3tNWS8pMx9KNXBXtOusiKwBXocrAaxQ1QN+10FghX99CrCv4G0dPm2y9I4i6cU+vxVoBTj11FNn/kXmgI3gnZ3tezq5ausTDIxmyYYhhwdSXLX1CT598WvHtScA9I9kiQgMjGZprq+iJhFjOJ1FRMgEIcPpLNkgZCgdEBWICgTASCbkT998StEb8MSur3XJGApkQiUq0N41BMApS6qKlhq37NhLJgg4MphlKD1WzZTKhjx9oG/c8cVmDrjxgWe5afvzBKGSjEXIBqGVTM28qliwEJE64LvAX6pqf2GzgqqqiMx5H11VvRm4GVzJYq4+ZyZPeDaLrTPZtZus5HDFt3YxnA4QcQ1tqSBkdDDNFd/aRU0iysqGqvxnpIOQiLjfOdXxKH0jGT656Wy27NjLrpd7iEWFFfXViEDXQIrRbMAt//MCr1m1pGTAyKVfevPDpIOQmkSM5w4NEISKAvt6Rjh1aQ3pbMCVdz5OQ3Wc1U01/HJ/L6OZkCA8+s+yd8SVbrbs2Fty3MVN258nVCUWEbKBcmQozbLaRMn3GFNpFQkWIhLHBYpvqur3fPIhETmpoBoq15q7H1hd8PZVPm0/riqqMH27T19V5PgFMdO2h4l10YcHU/QMZ+gbyXDpzQ9P6aZ5rJvs2v2io5ebtj9PNgxJRiMEoXtyvrijl6279jPsn8ZVXSkgZzgdEIRKPJoiCJTOwXR+n+AG3jVUx/OBOXfDf8sND7KkOs5gKssrvaOIuF5Rw+mg5L9n4b9L10CKlQ1JBkYzjGbD/OeFCvu6h90GcOpS95AwmHKBLqS4/tFsyVLmlh17yYYh8WgEQRABQldyspKpmS+zDha+Z9JXgKdV9V8Ldm0DLgOu97/vLkj/kIjciWvg7vMB5T7gn3O9poB3ANeqareI9IvIObjqrfcA/zbbfM/UxCqPXBVHuSe8wmqM5w71M5AKWFobZ1ltctxNE1hUDeGVDFylrt0NP9rD3sNDhKrEoxEChSODGZbVwS3/8wLN9UkE1+uhUK7s2lQT51B/iokP7e5Jf5iWbJJELDqukbguEaW9a5BUJgSBmAgREZIxyXeJLWwnePZQP4MF/2aHB1Ps7x0lFnE371zTX8QHBA2h1rdjuO62HJW/QqFSspS5r2eYZDRCOlBCDfOflQlLv8eYSqtEyeLNwJ8AT4rIz33a3+GCxF0icjnwEvBuv+8e4EKgHRgG3gfgg8Ingcf8cdepard/fQVwK1CN6wW1YD2hZtP2kLvJXnnn42TDkP6RLMlYlPqqeD7gADMKRnOh0j24Sl27PQf7CUJ3cw9CxT84c6AvBbhgkIgKqWD83VaBqliE5XVJDvWnin5mqNA1mGZJdWzcmIgjQ2mygas6QiGjSkSUlY01ZIOQXS/38GsfvZeRzPiywJHBDMlYlBX1VXT0DDOaHcuT4oJOxkeF0WzI3q5BltcliQmky1SMnrt2adH01U01DKeyDGcy+QCZ68VV6j3GVNqsg4Wq/g9jD3kTva3I8Qp8sMS5vgp8tUh6G3D2LLJZMVNteyj2RA74qqjA1T2Hyiu9o5y8BOqSMTp6XFfOxdIQPtNSVDHb93TSP5LhQN8IVbEozfVJ6qviHB5MkQ3H/oAmPn1HBPb3jrKsNsHhwRQT4gWj2ZCnDvSPK3VIwR1Vcec+qbE6H+xqE1FiUcn/G+SECgf7RsgEWrK6KBuEdA2kaK5PHpUXIB8owHXHTWVDN61I0bLRmNpEhK279hdtL9m8YS2bv7GTaATCcOw7LamJcc+TB47qInw8VFmaxcdGcE/TVGYPLTU1xPX3Pk08KiRjEVBX7ZFrXM0FnMU0KGu6A9FKDTrMXY+aRJSICOkgZH/PCIcHR+kZzpCICtGIHPXEIcCy2gQAfSMZElGKKlq9o2O35lCVwVQ2P+q6vXOQwwNpVxKIjP/U1CSBAlyJJx2EHOgdmeSofBYIQiVUHReUilnbXF9yRPjGdS3UJaNUxVyQq01EOXVpDQ1VcZ7rGrTV98y8sGAxTVNZQS/3RB6EyguHh3i5e5jO/lHau4aojkdZXpckRAlDBZThdEBHzwjPdQ7QO5ymbySzKKaynk7gmmzupNz1aK6v4uTGahLRCAoMpQLqq2Kc1FhFRFzAKNRcl2BlYzWnLKliNBviOw0dJRGNjAs0quOf4UOFF48M89yhAbJBSFbdALx0Nix7Ey/G9cbKzSkVOSrgFHIBY3zaxKNzb58sEJ+xooGVje76AbzSN8K+nhGiYvNImflhs87Ogbfc8CBRcXXuIq5aJJMNCdTdGBJR108+KKgmWdmYZFltctxgr6G0uzEXG408H1UNU5kBNRcMdr3c479HFfVVrhotN+gw11YxoTs1fSMZVvlqvcHRLF2DY43U8aiwbmVD/jzP+3EMxSpzquNRQg1JZZXqeOSodobCm3M0IvlurjMV99VXEYFELMpoJpjV+WoTUdY21zGczpKIRlhSkyjaffhvtj5B73AmH1zSgRvjsXppDfVVcQZGM3T2j5IKlDeuWWpVUmZGbNbZCio3x9PqphoO9afyvWRGM2G+fjtUV8+eaxcVXABJxsZ6zjRUx2mqTfLfV7+VzRvWsnXX/gWpaihXiiosTYTqqlte6R1lYDQDjD0pT1ZC2bxhLf0jGY4MpYlFhJj/iwwCpX8knS9Z5RS7KbuqHqhJRPn11U1EI0JNPEI8Mvae3M9sAwW4gLhuZT0ibqbamZyvMIDVJCIMp7P0j2ToGkzROTBKKpPlkReO8N5bH+PMj/6IX3T00lyXzLez5EpEocKB3hEGRjO80jtKJlSqYhGrkjIVZ8FimqYyVfXmDWvJhCFBGI4bGDZR7iYTjQgH+0bZ2zXInoP9HOh1VVJw9GynQah09o+y+Rs752QywomBEOCO1nP476vfyh2t5xStbqtJxHxV0FgbDIwPCKXaeTauayHppwd3XUOhMRklEYtwsD+VD1ATq6gKpQM32C0q8MLhQcJQGc6EZIpc+klOMy1Xn7+Oppp4yZ4d5eRKlACHhzIkohGW1SZorI4zMJKhazCTL2UNZwK+8GA7HT3DrGhIEo0I8UiEeERQXJXYKz3D5MLW8rqkVUmZirNgMU1Tmap647oWTm+um7Rffe5G4frfqytthEo04rpeDoxm2b6nc1wjc+7pMfcUX+mnx9yUGo+/3MPBvhEef7mHq7Y+UfL8hXnLtcOoKqlskH9S7hlK8Q93/5LaRJR4RI4qoWzf08mBgVS+hBUq9KWCfJDNBZSLXrOy9LUUOLkhSW0yRtdgetIn/WI9mKbr8GCKjeta+PTFr2XNspl3PMhlRRXauwbZ3zviBmwOuYGFIu7vJHdthjMhhwZSRBAiESHm20sESIeuevPkxmoafG86m07GVJItqzpNU12W85oLfo3Lb3+MhK82KDZgLMcPAHb18aGfBVVDNn9jJ8lYhKFUhnTWNYQjEAWS8Wi+K+v19z5dkYFzN/xoDz3DGaL+RqQKPcMZbvjRnqLnyw1sC0J1de3VcQZSWUQl34idCZUlfgR1JlA+uenso0onESAz4fqE6to1rtr6hOs2O5SmmJMakzRWxXnhyDDpbJi/rpN3VJ25WMS1FeQMFyu+zEAmUDJBwOHBowcXirhgEYRKJnBzWeXGWUREOGVpFQf7U6xsrDrhp5Mxc8dKFtOUq3/vH8nwSt8I2UDzT3+FT/m50kWkTL1H7sYguBura+fQfH14RKBnOMtoNsjfILIKqWzAnoP9dPSM8MyhynSf3Ht4iIi4G5DguvZGxKVPtH1PZ35gmwCZIKRnOENdMsaWP34DS2oSxKOueu2ZQwMc7BslEwRHVYvs6xkmEin+Z6i4YPVi9zDDqSxRfylj4gbjxaNC10CKI0PpOQsOOdXxKNVx165U6/vw5kqZlaraAvd98+fzXygWiRCqG4tzRksdEV8dif/72N87StSX2hZDLzpzfLJgMU25+vdDA6OuKsn/Z2Vj1VHVUddc8Gu01Fdx2vJamuviJc5YwPdMi/n6/0Q0Qsb3eFEdq5IA1x0zKkLKP01nA51R98kbH3iW1/zjfbzq7+4hlS0+0V0xW3bspaE6zqqmauK+FBGLuF5TW3bs5ZEXjnCgL0UqGxL1pavDA+l8W0zO6qYaN7fThDuu+O+Ue6JOB2MNyVl1gTXXTtJQHWdlY9X47rNT+hZTN5IJGM0EZAPl/W85DRirhmuuS+bzPFuZIMx3xXXXFBRXMn3/W07j6vPX0dJQxbLaBCEQqAvWjdUxBIpW9RlTCVYNNU25OZ42f2MnoSrJaCQ/GllVx9URF84Hlc6GxCIZP0320bcyxdWnB9mCag2JkA2VWFQIFU5ZUs1LR4bzx+cmsIvg6tFzjcupbHBUtVgxH7lzF9//uZtFPterNVBIZwMXANSVfF69/OjR6bte7iEIQ5IFo7H7R9J09I6SiI09gwSh5sdQhOLGNhTavGEtu17uyZeiCmOVMr7Kp1BUhIbqGN1DmfwTf3NdYtxEgnOhoTrGa1YtAaA+GaO9c5BAlXi0+L/rdFXFIpy0pJqu/lGyoZtdtzYe5f1vOY0rzzsDgOtwU8aEquNGww+nszTVJvnRR2x9FFN5FixmYOO6Fl5/atNR034cGUoxlAp4yw0Pjms7KJzWeudL3VOqMongDgpDJatQFXdzSBVOWpcT4mZfzc2equqeUC+/vY0zWuq4+vx1RWdQ/Y+fH8hvF54zUIioa2xfkoxzzQW/Nu59H9u229eju9HYLx0ZJhIRwlDHTQ/iv4JrrA58O0JsfOY3rmvhgxtf5WacLchEueuTCUK6hzKsXV7LSCagJhFjRWM1fSOZo+aQmg23PrebGiQ3FiJXausaTOXHW0y1RFbus1oaXLtDc0NVyYWxNq5roaE6zqlLa8aNXbEGbTOXLFjM0MQpxwtXZys16Z4bXHdkSucPcU+V4G7eQ+mAJ/f3lTxecTfQ3CR8sYirS3/h8FC+kXgwHeSD2JYde4vekHOBLCKCKvkqlomD7xqqYnQPZ/L9/XM3y0ygDIxm8oP4cvfQiA9ig+mA1/zjfYAbeJdbJS4XMFLZqTUY576vqtI/4sZ1ZIOwooEi970EJa3uppy7IW/ZsZfG6ji1iRiHB1NkgqDMmcYIbpGkg/2jbmCmQjIqnLSkOj+gsdyNf3VTDS8eGaR/JEs6CElEIzRUx1izrG5W39eYUixYzNDEldOGUgHNdQma690iPMUm3XtobzeJqJSsWpmt3MAz8N0t/UI53UNpjgyliQgc6huh7aVusiXykEvNTcU9kgn4izt2MZp1U2MH/km6dyRLWGT0vwIvdw/TXJfMr0GRazTPqiLq1m6IR4WRDDx9oM9V6YVK1M8RFY9IvoqtnPauoXyngJ6RbMUbul2PLkj6ealyPYz29QwTFTg8mCYdhNP6zPqqGP9zzdgcm7kld4NQ2ds1SDoIiYpw2vLakuc4d+1SHn2x219bV3rrHEhz6W/YLLRmblgD9yxsXNeSH7DWUB1nuX8Kz5n4dLivZ5jG6vmJz5lQGc24sRuB5rqiuhtfJph81HEENxX3YCpLNlAGUq7ba8wPAgvU3ZxK1byECt25sQL5NCUmY20jsUgEDZU+H3QyoZsrKwyVzCQDGQup4kYzB8rhoQzNdYk56xHlqvrGehjVJaLs7x11QXcaU+YIcNbJjePScqPYO3pG8qXDbKh0DaZK9mp7aG839ckoQaikskoQKvXJKPc8eWDS2QWMmSkLFhUylUn3VjfVMJAKJp14bi5NtUCTiEXyjeUH+0fz6VN92gdflRURVjQkOevkxvzYjVDHRlGH/rjcjEW+WJ4AACAASURBVLMi4tbDnmI+dcLvzoHia1oUEsh3wZ2OTKDjehjl2gpCtOhI8VIU2Nc9NO5mvnFdC8tqE8SiLhjHoxFOWVJNY3W8ZK+2Zw/1M5QOiEcirhtxJEL/aJZnO20WWjM3LFhUyFSmLj937dL80/5iFJOxtgURGEkHU25DKCTi6uQjuIWHnuscIMJYN8/c1B2h7w6sCsmY632VG5cyk3A6lcuaKxlNh+DyVzjdSddgCtAZ9YA6PJg+6mY+mA54dXMd61Y2sLa5jobq+KTtFrnPjUQEETeiW30hx2ahNXPhmAkWInK+iDwjIu0ics1C52eiqUy6t3XXfhLRo9dtWCyy6v4gsqpksuGk6zpMRvADxaLCqiVVbiW60E0F0lQTB4VsGOansVB1k/OdvKQqv8ZHLIKfWLAyVysqrhE5JyLuu07l9BGB0wqm9di+p5OB0ey4RZtyppLbwA+6zAZuypbN39iZnw2g0GQjsBNuAIafyFDzExoWfp/+kQwH+0Z59MXuSaukyk2MaQwcIw3cIhIFbgLeDnQAj4nINlV9amFzNl5hN9mJcqN9T2qsdiun+fr2xSabGyY+Q7mSAgIr6qtoqI7TUJ0YN/32c50DDKVcyStQCPxiQkvrErQ0VHHx60/hpu3PE/qSSCW4zxn7XuHEOqxJNNUm8t2Ht+/p5Mo7H8+3q0x8eyzCpNVSUXFzOOVmAFBVsqHr2dU/mvUz9kp+MOOm155c9Dynt9Qf1RtKo27UPZA/P7ggWWpJ3EovnWuOX8dKyeKNQLuq7lXVNHAnsGmyN7z00kv84Ac/ACCbzdLa2so999wDwOjoKK2trfz4xz8GYHBwkNbWVh588EEAent7aW1tZceOHQAcPnyY1tZWfvaznwFw8OBBWltbeeSRRwDo6OigtbWVnTt3AvDiiy/S2trKE088AUB7ezvt3/9XYn37aaiOsyLspr7tq8QH3TiHWN9+Gtu+RnTwkNvufdltDx122z0v0tj2NSLDbkny+JHn3fao60obP/wcjW1fQ1JudHSi6xm3nXbTdCQ6n3LbWdf+kDj4SxrbvgaBa4ROHnjCbYeuzSX5yuNu20vub6Nh12357ap9j9Lw+NfHtl9+iPqffwtwN8+al/+XJU9+m8ODKZ460M/z//tDuh68lb2Hh9i8YS3vjO6m9snv5ifBq3n+QeK/+B4H+1K84dRGInt+TP3T24jgBuXVPHsftXt+mP+82mfupfaZsWXYa/f8kJpn78tv1z29jZr2+8e2d3+fmucfHNv+5Xep3rs9v13/5Heofem/84Gp/ok7qX7pfwFXZbbqme9w4PGfjI0xeehr1Ox/LP/+hl23kdzv1k7JhNDY9jVO7ttNS10CCQMa275G8qD7W5AwQ+1jX6Xn2TbXuyw9SmPb14gfcs89w4MDxB66herDz7C0Ns53/vdp3v3H7zvqb++3l/YRj0ZpiQ6x/Oe3sTzl/rbqMj103nsj3fuec/kf6KSu7WvE+l8hHhVu/N4OWltbeeaZZwD4wtafktr+JeJDXYgIsd59pLZ/iX/b9hAAO3fupLW1lY6ODgAeeeQRWltbOXjwIAA/+9nPaG1t5fBh97e6Y4c7f29vLwAPPvggra2tDA4OAvDjH/+Y1tZWRkfd3+I999xDa2sr2axb2eoHP/gBra2t+Wv7/e9/nyuuuCK//Z3vfIcrr7wyv33HHXfwkY98JL/99a9/nauuuiq/feutt3Lttdfmt2+55RY++tGP5re//OUv84lPfCK//cUvfpFPfepT+e3Pf/7z3HDDDfntz372s3z2s5/Nb99www18/vOfz29/6lOf4otf/GJ++xOf+ARf/vKX89sf/ehHueWWW/Lb1157Lbfeemt++6qrruLrXx/7f+sjH/kId9xxR377yiuv5Dvf+U5++4orruD73/9+fru1tXXW971SjomSBXAKsK9guwN408SDRKQVaAVobGycuHtBVcUijGYDEkBdVYwwHqVxSTWdkahrLKBg7ehj2OqmaiLdSXp63frToV8/whVYlI9t2w3PHSYTuhkTC5/MFfjhkwdZ+uKLjGYrP2ZiMvXJGMuW1pAJAob9nFjxqLCsNsGBvlHaDw2wzZcORYqXCnPtMSIQhCErGqupjgv9EfdUlogIGkI6G5LyswwX+yePAC1Lqqmqq2Kwb5gDvoSwfU8nX7zncV7a18uhXxzg4g2/yY6fD/FyqCypTvCJi19L5ysd/NtTEXqCkHhEWFafJIiMjRE5dHiU5QWfdWhgNF8ayX++CAf7yy8ba04sx8RKeSJyMXC+qr7fb/8J8CZV/VCp9yzkSnnFlFp17uLXn8LWXfvdjKMDaTKLsGpqOtyCQ1L0eySjwqqlNXT0jBCq5hctyt2rCv8Up9MraraiEeHkxio+uelsrrzzcYbTAclYhOV1SRqq4+NW/Iv6iftKBYtIxPXoClX5lWW1+X/rvpEMAsSiQt9wZtxstRO/vwBnn9Lo09yKgp/cdHbZVQsL5cZuFI6mz32PwlHhUz3OnDiO9ZXy9gOrC7ZX+bRjRqkG8CvPO4PrLjqLNcvqaKyJ53sKCSxYF9vZyISUDHgpv/pdKhuSCcambc/14ikk81jMigocGUy7hubRLBGU5vokIrC3a5CXjgyx6+Ue6pMxDg2kiPouvhMpY/9DhQq9Q2l2H+jn+a4huofSBKGyvK6KV7XUj1sHY+L3V2DPwX72dg1yZCjFqqaaKa2jUmgqvfMKjzs8OMrznQM8daCPjp4Rzl1rg/vMeMdKySIGPAu8DRckHgP+UFV3l3rPYitZTFWuBJLOBvQMZ2bUddVMX1RcCWPi6Pq4770WiQh1yRjdfjr0UqUeV30VIRuGBCH5KcxTfh3dFfVJWhrcKP+nD/Tl1zKZOIliMuYmjwwVPvzWV3PXzo6S65j/99VvLZqX3BQtHT3D+RULi5VCbnzgWW7a/jxBqCRjEeqrYiRiUZu19gRVqmRxTLRZqGpWRD4E3Idb++erkwWKSsv9TzfbxYWmonAakZ6Xe+bkM8x4USG/2NPE/k258QxJ3I2/KhY9avBlodxI+dAHgZhfqyMibp6srsFUPlg01yc5PJgmKkLWvyE35iRQNwV7fVWMh/Z2s7qp5qjqonKLG03WO6/QQ3u7WdVUfVRVVOFUNcYcK9VQqOo9qnqGqr5KVT9V/h2VMZU1tystN41Ic31yautgTHDsVV4trEAhDMP82I9i0qFyqC9FNAIl1moC/BKvS6rcGtv+ZNmCqVFChf6RNMPpLPFolL/47VfzulObOKnRrQty6tJqTl9Rnx+ct7wuSUfP8JSrlWaicHncHJvB1kx0zASLhTLduuJKWt1UQ0N1wnXBnMb7Fn/F4uytqE9OaUBduUNy58iWmeJJ1U1Pkg2VJVWTFMjVrWKYW6gqG4RHteG81D1C33Ca2kSUu3a6Lqmf3HQ2py2r4dBAKt9e0T+SyZceyg36nI2pTFVjzDFRDbWQCtfczpmvp67cNOj11XFaGqryvWqa65LsOejGVEQjgqrOW8+hxaJrMEUiGpl0QkOYwrohfvnaqTbdjWTCSduRFOgcSFOdiDKcDo6amTcWcZ93eChDPBZhWW2SzoFR/mbrE2SCML86YCYI2d87Qk08QjwiR62RUkkTp9vP9bSyJVlNIStZlLGQT13FniY/c/FrufcvN3DO2mWsaEgSi8gJFygA3/hbfoS3AEtKzPQbjwjxaISonxNrqqW38r2blbXLa1laM/4hI+Y/L6d/JJsvrQ6msqQy4bhlaiMCI1lXMpnLKtC5LLWY44eVLMpY6KeuUo2UuXxBZl7ysRhNZV0QBU5qrOZP37ySh/Z288gLR1DNzTyr4x4EYhGhNh6hPxWULJHkAspkn1wVc8u8ntJUw3B6gNFsSFXcrRcOYxMopgumYg/83FmFMoG6AYCB8kLfkFvnIiJcf+/TFb+RT7Ux3Jy4rGRRxmJ96srly0/DhOCelBPRE+efdColgURUWFKT4MrzzuCO1nN402muRCZy9BxO2VDpmyRQuHmdhMkucVRcL6ecFQ3udS4YhH7UdsTPEZV/X8TN6/SKH/AX9WuHhAodvSNkAyUqbuna57oGbbI/M++sZDEFi/WpK7cW+AuHB91KeMhxMWXIVOVu6pOtjhco49qXNm9Yy4e//XjRyf6iEZl0Le1YxC192tEzQiLqPjc3JUkEt056Y00svzQquC65q5uq6Rxw63UnYxGqEzEGUgEN1TFUXemmLhmjbyQDKBHctCC57xWGSsQNjXcPBSLWrdXMOwsWx7hcddSy2gQDo1lS2dAFDD+NRql738RBYMeyyYJFGCoahlx688Ps6xmmPhljYCRb/BxlWrkDdU/8TTVxltUmGEoH4wa75bpZT6yy/OSmswHGDZA7d+1S7v3lQZ7rdBPsnbashtFMlkxW8w3osajkVzVUND/Se2Vj0rq1mnlnweIYN3Et8NyN6Kbtz+dvOrnCRjQiJKLCiJ/+OqxAy/iS6ji9IwvbbjLZGHcFDg2mSSbcOJn2zkFCjg4wuZnZC9ML2yfcJIERWuqr+OjvnFn0qb7Yv0Vh76WJU4Nv3bWfVU3V+cAymnHrnCeiEb+ueO7TXTVWIhqhuT5JNCK0+LXejZkvFiyOA6WqyT73k+fyq95FxNWJN9bEyQykKzYYIxOEJGORRTstieAaig/2jdJcn8yv1lfq6zdUx0hlAkazYzPiRgWW1ib49MWvLVv1M9Uqy8LxO+BWt4uIkA7dWuW5SBUVN9XIqUtrrFurWVAWLI5TV553BgA3bX+ebBiSiLpAEY9GeXVLHb0jaQ72pWYdM1LZgMUwZjzqq9Umfp/cdiob8ErvqGskFjcuJSZ+sSdvdVN1vsro+nuf5oUjrqpn7fJarj5/XUXbCIqN31H/PWIRyS9otLKhmqF0QEt9Vdk5noyZSxYsjmNXnncGr1m15KhqEYCPbdtNMjY24d1Mg4YrUCx840e5GrVsCLFISEQiiAiJiJvwD18ld+HZKzjYn+Yf7v4lq5tquOaCX5vTG3KxuZ6iESEqwtrmunzacDrL6S01Nl24WXAWLI5zpapFrgNu+NEenu0cJBkXVtQn/ToT07v1RyN+Wos5roWKR4qvkVFOYbtDoKBhyNKaxLgG6nPXLmXrrv3zurRosfE7dckYAjaS2ixKx8QU5TNxrE5RPt8Kp7HuG8lQk4gyMJolG+qU2iFyE9Cls8GUR5JHBZpq4hwemrxhPNe2kFs0KCJjXVtFxmaEnWhiT6+Y7xKrQENVjBsved24ILBQCwBNnEL83LVLuefJA3Na/WVMOcf0FOVm7hSWPHJdP0ezKWJ+xbdi9+OlNXG6hzN+VTxneqURoSYZZ1UsQudgOn/Tj4rr2ZRfFMmnNVbHGUoH1CejLK9LIiI87efGys21VBgcJj7/BKGbbykWjRwVKGDh5v8qdu3jUeH0ljpGMgFD6dJToRsz3yxYmLxc18/c0qJV8ShRgcF0kL8ZN1TF+NWVDTyxr9eto+1NtYCaiLqRyfGokEgkiESj45YKzU2U2DUwSjpQErEIp7fUH9Wo+6v/cC+qStTPFx6Emp8+IzIhyOVGQn9w46uKPqXPZK2ISivWO8rWlDCLiQULM87GdS3ceMnryq73fOMDz/KFB9vJhiERGStZNCSj9KdKPxGn/ayq2SAknQ355Kazx1XFlBrDUGj7nk4/yZ4iQUDMT5WRKwlFIuL+sP2MsolohOp4JN9DbKKFnv8LFnZ2Y2OmYlbBQkQ+DfwekAaeB96nqr1+37XA5UAAXKmq9/n084Ev4Fa8u0VVr/fppwF3AsuAncCfqGpaRJLA7cAbgCPAH6jqi7PJt5lcucFlMNY195b/eYGhdEBNwpVCmhuqSHUPk/Yjj4uNaQjVremwaknVtKdSyVXXNFbHSWVTqLo1vxNRaKpNsLw2QSbUou0Ps/m+c20xlG6MmcxsSxb3A9f6ZU9vAK4FrhaRM4FLgLOAk4EHRCT3WHcT8HagA3hMRLap6lPADcDnVPVOEfkyLtB8yf/uUdVXi8gl/rg/mGW+TRnlbuLb93Ty0N5uGqrjnHVyY/4pfMuOvfQNpxlIBSytjZOIRni5e2RcwMgFkEMDKbbv6ZzWTTlXXdNYXUUyFuXwYIpUNqQqHuUzF78WYEalhIWe/2sxlG6MmcyspihV1R+ram6inYeBVf71JuBOVU2p6gtAO/BG/9OuqntVNY0rSWwStwr9W4Gt/v23Ae8qONdt/vVW4G0iJ9J0eYtPqaVmAe5oPYe2j76DLX/8BtYsqyNUSMQixCKuB5PgfscjgirTXnGwcAnQhuo4a5vrWLeynsbqeP6Gv1CzBG/f08mlNz/MW254kEtvfnhaM8Mu1tmNjcmpZJvFnwLf9q9PwQWPnA6fBrBvQvqbcFVPvQWBp/D4U3Lv8SWYPn/84YkZEJFWoBXg1FNPneXXMaVMpTG28En90psf5tEXj5CMFazpECqxqEy7Tn4q1TULUUoo7M0007EaC126MWYyZUsWIvKAiPyyyM+mgmP+HsgC35zLzJajqjer6npVXd/c3LyQWTmuFT7d50zWGLt5w1pikci4NR1ClPqq2LTr5DdvWEsmUIbTWVTd78VQXVMYQAdTWQ72jbK/d5gr73zc1p4wx4WyJQtVPW+y/SLyXuB3gbfp2Ai//cDqgsNW+TRKpB8BlohIzJcuCo/PnatDRGJAoz/eLJDpNsZuXNfCBze+ys9T5dZ0qK+Kk4hF8zf53AC1fT3Dk641PR+N0VPNS6Fcb6aB0Qyv9I4ifo6n4XQw56PBjZkPs+0NdT7wt8BvqWrhY+U24Fsi8q+4Bu7TgUdxVdan+55P+3GN4H+oqioiPwUuxrVjXAbcXXCuy4CH/P4HC4LSvJvJjeR4M5PG2FLzVBWuAzHVKpy5rK6ZaXVSLoB2DaTys/yGISRjQjxqixWZY99s2yy+CCSB+32b88Oq+mequltE7gKewlVPfVBVAwAR+RBwH67r7FdVdbc/19XAnSLyT8DjwFd8+leAr4tIO9CNCzALohL10seDmT7dl7rJL6YBaTPNSy6AjmbduI8whBBleV2VjZcwx4VZBQtVffUk+z4FfKpI+j3APUXS9+J6S01MHwV+fzb5rJTFdFNbaJV8ul9MA9JmmpeJo9+TMWF5XRUN1XGG01kbL2GOebPqOnuimW7Drpma1U01jGTGj/peqAFps8lLbvT7yUuqWdlYRX1VbNE0wBszWxYspmEx3dSOJ4uph9Ns82LjJczxyuaGmgYbZTs3FsN0G5XMi42XMMcjW89imiauQXAi9oYyxhy/bD2LCrGnRjMfrIu2WWyszcKYRabU3Fs2EtwsJAsWxiwyhV20Rdzv3MA+YxaKBQtjFhnrom0WIwsWxiwy1kXbLEYWLIxZZGY61mM262kYU44FC2MWmZkM7LNGcTPXrOusMYvQdLto27xlZq5ZycKY44A1ipu5ZsHCmOOANYqbuWbBwpjjwGKajNEcnyxYGHMcsNluzVyrSAO3iPw18BmgWVUPi1s27wvAhcAw8F5V3eWPvQz4B//Wf1LV23z6G4BbgWrc4kgf9sutLgW+DawBXgTerao9lci3MccTm7fMzKVZlyxEZDXwDuDlguQLcOtunw60Al/yxy4FPg68Cbcq3sdFpMm/50vABwred75Pvwb4iaqeDvzEbxtjjJlHlaiG+hzwt0DhXOebgNvVeRhYIiInAe8E7lfVbl86uB843+9rUNWH1c2ZfjvwroJz3eZf31aQbowxZp7MKliIyCZgv6o+MWHXKcC+gu0OnzZZekeRdIAVqnrAvz4IrJgkP60i0iYibV1dXdP9OsYYY0oo22YhIg8AK4vs+nvg73BVUPPCt2GUXK1JVW8Gbga3+NF85csYY453ZYOFqp5XLF1E/i/gNOAJ157NKmCXiLwR2A+sLjh8lU/bD2yckL7dp68qcjzAIRE5SVUP+Ooqm7/AGGPm2YyroVT1SVVtUdU1qroGV3X0elU9CGwD3iPOOUCfr0q6D3iHiDT5hu13APf5ff0ico7vSfUe4G7/UduAy/zrywrSjTHGzJO5mhvqHly32XZc19n3Aahqt4h8EnjMH3edqnb711cw1nX2Xv8DcD1wl4hcDrwEvHuO8myMMaYEcZ2Pjj/r16/Xtra2hc6GMcYcU0Rkp6qun5huI7iNMcaUZcHCGGNMWRYsjDHGlGXBwhhjTFkWLIwxxpRlwcIYY0xZFiyMMcaUZcHCGGNMWRYsjDHGlGXBwhhjTFkWLIwxxpRlwcIYY0xZFiyMMcaUZcHCGGNMWRYsjDHGlGXBwhhjTFmzDhYi8hciskdEdovIvxSkXysi7SLyjIi8syD9fJ/WLiLXFKSfJiKP+PRvi0jCpyf9drvfv2a2eTbGGDM9swoWIvLbwCbgtap6FvAZn34mcAlwFnA+8O8iEhWRKHATcAFwJnCpPxbgBuBzqvpqoAe43KdfDvT49M/544wxxsyj2ZYs/hy4XlVTAKra6dM3AXeqakpVX8Ctxf1G/9OuqntVNQ3cCWwSEQHeCmz1778NeFfBuW7zr7cCb/PHG2OMmSezDRZnAP/HVw/9l4j8hk8/BdhXcFyHTyuVvgzoVdXshPRx5/L7+/zxRxGRVhFpE5G2rq6uWX41Y4wxObFyB4jIA8DKIrv+3r9/KXAO8BvAXSKytqI5nAZVvRm4GWD9+vW6UPkwxpjjTdlgoarnldonIn8OfE9VFXhUREJgObAfWF1w6CqfRon0I8ASEYn50kPh8blzdYhIDGj0xxtjjJkns62G+g/gtwFE5AwgARwGtgGX+J5MpwGnA48CjwGn+55PCVwj+DYfbH4KXOzPexlwt3+9zW/j9z/ojzfGGDNPypYsyvgq8FUR+SWQBi7zN/LdInIX8BSQBT6oqgGAiHwIuA+IAl9V1d3+XFcDd4rIPwGPA1/x6V8Bvi4i7UA3LsAYY4yZR3K8PqSvX79e29raFjobxhhzTBGRnaq6fmK6jeA2xhhTlgULY4wxZVmwMMYYU5YFC2OMMWVZsDDGGFOWBQtjjDFlWbAwxhhTlgULY4wxZVmwMMYYU5YFC2OMMWXNdm4oY4wx82D7nk627NjLvp5hVjfVsHnDWjaua5m3z7eShTHGLHLb93TysW276RwYZUl1nM6BUT62bTfb93SWf3OFWLAwxphFbsuOvcSjQk0ihoj7HY8KW3bsnbc8WDWUMcYscvt6hllSHR+XVh2P0tEzPC5tLquqrGRhjDGL3OqmGkYywbi0kUzAqqaa/PZcV1VZsDDGmEVu84a1ZAJlOJ1F1f3OBMrmDWvzx8x1VdWsgoWI/LqIPCwiPxeRNhF5o08XEblRRNpF5Bci8vqC91wmIs/5n8sK0t8gIk/699woIuLTl4rI/f74+0WkaTZ5NsaYY83GdS1cd9FZtNRX0TeSoaW+iusuOmtcFdO+nmGq49Fx7ytWVTVTs22z+BfgE6p6r4hc6Lc3Ahfg1t0+HXgT8CXgTSKyFPg4sB5QYKeIbFPVHn/MB4BHgHuA84F7gWuAn6jq9SJyjd++epb5NsaYY8rGdS2Ttj+sbqqhc2CUmsTYbX1iVdVszLYaSoEG/7oReMW/3gTcrs7DwBIROQl4J3C/qnb7AHE/cL7f16CqD/s1vG8H3lVwrtv869sK0o0xxnhTqaqajdmWLP4SuE9EPoMLPL/p008B9hUc1+HTJkvvKJIOsEJVD/jXB4EVpTIjIq1AK8Cpp546g69jjDHHpo3rWrgO13bR0TPMqgr3hiobLETkAWBlkV1/D7wN+IiqfldE3g18BTivIjkrQlVVRHSS/TcDNwOsX7++5HHGGHM8KldVNRtlg4Wqlrz5i8jtwIf95neAW/zr/cDqgkNX+bT9uDaNwvTtPn1VkeMBDonISap6wFdXzd+QRWOMMcDs2yxeAX7Lv34r8Jx/vQ14j+8VdQ7Q56uS7gPeISJNvlfTO4D7/L5+ETnH94J6D3B3wblyvaYuK0g3xhgzT2bbZvEB4AsiEgNG8e0FuN5MFwLtwDDwPgBV7RaRTwKP+eOuU9Vu//oK4FagGtcL6l6ffj1wl4hcDrwEvHuWeTbGGDNN4jofHX/Wr1+vbW1tC50NY4w5pojITlVdPzHdRnAbY4wp67gtWYhIF67aaiqWA4fnMDszZfmaHsvX9Fi+pudEydevqGrzxMTjNlhMh4i0FSt2LTTL1/RYvqbH8jU9J3q+rBrKGGNMWRYsjDHGlGXBwrl5oTNQguVreixf02P5mp4TOl/WZmGMMaYsK1kYY4wpy4KFMcaY8lT1hP3BLbD0DG5akmvm6DNWAz8FngJ2Ax/26Utx63k85383+XQBbvR5+gXw+oJzXeaPfw64rCD9DcCT/j034qsXp5C3KPA48EO/fRpu8al24NtAwqcn/Xa737+m4BzX+vRngHdW4toCS4CtwB7gaeDcRXK9PuL/DX8J3AFULcQ1A76Km1DzlwVpc359Sn1GmXx92v87/gL4PrBkptdhJte6VL4K9v01bl2e5Yvhevn0v/DXbDfwL/N9vUr+3U335ne8/OBulM8Da4EE8ARw5hx8zkm5PzigHngWOBO3quA1Pv0a4Ab/+kLcvFgCnAM8UvCHt9f/bvKvczeER/2x4t97wRTz9lfAtxgLFncBl/jXXwb+3L++Aviyf30J8G3/+kx/3ZL+D/N5f11ndW1xi1y9379O4ILHgl4v3PoqLwDVBdfqvQtxzYANwOsZf1Oe8+tT6jPK5OsdQMy/vqEgX9O+DtO91pPly6evxk1u+hJjwWKhr9dvAw8ASb/dMt/Xq+T/A5W+OR4rP7in1fsKtq8Frp2Hz70beDvuSeAkn3YS8Ix/vQW4tOD4Z/z+S4EtBelbfNpJwJ6C9HHHTZKPVcBPcLMF/9D/oR9m7H/s/PXx/0Od61/H/HEy8ZrljpvNtcWtNOdx1AAABVxJREFUuPgCE572F8H1yi3ctdRfgx/iVn5ckGsGrGH8TWbOr0+pz5gsXxP2/d/AN4t9v3LXYSZ/n+XyhSu9vhZ4kbFgsaDXC3eDP6/IcfN6vYr9nMhtFqVW7ZszIrIGeB2u6FdqBcBKrjI4mc8DfwuEfnsZ0Kuq2SLnyX+239/nj59uXqfiNKAL+JqIPC4it4hILQt8vVR1P/AZ4GXgAO4a7GRxXDOYn+sz5VUrS/hTxmaTnm6+ZvL3WZKIbAL2q+oTE3Yt9PU6A/g/IvKIiPyXiPzGDPNV0esF1sA9b0SkDvgu8Jeq2l+4T12I13nMy+8Cnaq6c74+cxpiuKL5l1T1dcAQrgifN9/XC8Cvv7IJF8xOBmpxdcWLznxcn+l+hoj8PZAFvjlnmZp6XmqAvwM+Nl+fOY3rFcOVXs8BrsItzyBzmbepOpGDRanV/CpOROK4QPFNVf2eTz7kV/5jwgqAk60yWCq91CqDpbwZuEhEXgTuxFVFfQFY4tcmmXie/Gf7/Y3AkRnkdSo6gA5VfcRvb8UFj4W8XuCWC35BVbtUNQN8D3cdF8M1g/m5PqU+Y1Ii8l7gd4E/8jfNmeTrCNO/1qW8Chf0n/D/D6wCdonIyhnkq9LXqwP4njqP4kr+y2eQr0peL6dcPdXx+oOL4HtxfzS5hqGz5uBzBLgd+PyE9E8zvvHrX/zr32F8A9ujPn0pri6/yf+8ACz1+yY2sF04jfxtZKyB+zuMbxC7wr/+IOMbxO7yr89ifKPbXlyD26yuLfDfwK/61//or9WCXi/gTbjeKTX+fbfheq0syDXj6LruOb8+pT6jTL7Ox/UEbJ5w3LSvw3Sv9WT5mrDvRcbaLBb6ev0ZblE4cFVS+/x55/V6Fb1OlbopHos/uJ4Pz+J6E/z9HH3GW3DFz18AP/c/F+LqCH+C61b3QMEfngA3+Tw9Cf9/e/fzElUUhnH8+4iUm1q0a9PCQFr0Q8EIqoX1P4QLISw3LYKgTYtW/QUtDIk2LapVLYQ2kYuCKGKsSCxQmsggci0GYZGnxXmHrsVwbNSZhc8HBOfOue/cebl3XodzfC+DlVjnycvd6sC5yvZB8nLOj8AN1rkUNPYd4k+x6I0Tvx4nWmNFRk88rsfzvZX9r8brzlNZVbSR3AL9wKvI2WRcnB3PF3CNvKTxHXAnLty254y8bHcR+En+S3SsHflp9hqF46qTP/Aa5/7NVvPQSq6bHddfzy+wdulsJ/O1A7gb8d4Ap9udr2Y/bvdhZmZF23nOwszM1snFwszMilwszMysyMXCzMyKXCzMzKzIxcLsP0j61uljMOsEFwszMytysTBrgaQhSU8lPZA0J+leo4ePpKOSXkiakVSTtEtSj6TbkmajQeKpGDsqaVLSlKQFSRclXY4xLyXtiXH7JT2S9FrSM0kHOvn+bfvpLg8xsyYGyG0YvgLPgROSauQbywynlKYl7Qa+A5fI/eQOxQf9Y0l9EedgxOoh/1ftlZTSgKTrwFlyh+BbwIWU0gdJx4AJck8vs7ZwsTBrXS2l9AVA0ltyn58lYDGlNA2QosOwpJPAeGybk/SZ3PsH4ElKaRlYlrQEPIzts8Dh6Fh8HLhfaUC6c4vfm9kaLhZmrVup/P6L1q+napzVyuPViNlFvjdBf4vxzTbMcxZmm2se2Nu4aU3MV3STO+mOxLY+YF+MLYpvJ58knYn9JenIVhy8WTMuFmabKKX0AxgGxiXNAFPkuYgJoEvSLHlOYzSltNI80j9GgLGI+Z58IyaztnHXWTMzK/I3CzMzK3KxMDOzIhcLMzMrcrEwM7MiFwszMytysTAzsyIXCzMzK/oNz1V7T5ZVtWgAAAAASUVORK5CYII=\n" + }, + "metadata": { + "needs_background": "light" + } + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "---------------------------\n", + "\n", + "Resultados do Polinomial de Grau: 2\n", + "\n", + "Resultado do conjunto de treino - Grau 2 :\n", + "As variáveis explicativas do meu modelo explicam 81.69 % das variações na renda dos clientes.\n", + "O erro médio absoluto do modelo é: 6953.15\n", + "O erro médio quadrático do modelo é: 85098902.1\n", + "A raiz quadrada do erro médio quadrático é: 12704.330739940186\n", + "Acurácia: 0.75\n", + "\n", + "Veja o comportamento dos resíduos:\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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\n" + }, + "metadata": { + "needs_background": "light" + } + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "\n", + "Resultado do conjunto de teste - Grau 2 :\n", + "As variáveis explicativas do meu modelo explicam 65.3 % das variações na renda dos clientes.\n", + "O erro médio absoluto do modelo é: 8150.09\n", + "O erro médio quadrático do modelo é: 160362399.84\n", + "A raiz quadrada do erro médio quadrático é: 12704.330739940186\n", + "Acurácia: 0.65\n", + "\n", + "Veja o comportamento dos resíduos:\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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\n" + }, + "metadata": { + "needs_background": "light" + } + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "---------------------------\n", + "\n", + "Resultados do Polinomial de Grau: 3\n", + "\n", + "Resultado do conjunto de treino - Grau 3 :\n", + "As variáveis explicativas do meu modelo explicam 86.68 % das variações na renda dos clientes.\n", + "O erro médio absoluto do modelo é: 6112.91\n", + "O erro médio quadrático do modelo é: 61903278.53\n", + "A raiz quadrada do erro médio quadrático é: 12704.330739940186\n", + "Acurácia: 0.75\n", + "\n", + "Veja o comportamento dos resíduos:\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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\n" + }, + "metadata": { + "needs_background": "light" + } + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "\n", + "Resultado do conjunto de teste - Grau 3 :\n", + "As variáveis explicativas do meu modelo explicam 51.88 % das variações na renda dos clientes.\n", + "O erro médio absoluto do modelo é: 8368.21\n", + "O erro médio quadrático do modelo é: 222418278.71\n", + "A raiz quadrada do erro médio quadrático é: 12704.330739940186\n", + "Acurácia: 0.65\n", + "\n", + "Veja o comportamento dos resíduos:\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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\n" + }, + "metadata": { + "needs_background": "light" + } + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "---------------------------\n", + "\n", + "Resultados do Polinomial de Grau: 4\n", + "\n", + "Resultado do conjunto de treino - Grau 4 :\n", + "As variáveis explicativas do meu modelo explicam 91.45 % das variações na renda dos clientes.\n", + "O erro médio absoluto do modelo é: 4725.18\n", + "O erro médio quadrático do modelo é: 39752173.18\n", + "A raiz quadrada do erro médio quadrático é: 12704.330739940186\n", + "Acurácia: 0.75\n", + "\n", + "Veja o comportamento dos resíduos:\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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\n" + }, + "metadata": { + "needs_background": "light" + } + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "\n", + "Resultado do conjunto de teste - Grau 4 :\n", + "As variáveis explicativas do meu modelo explicam -284738667.91 % das variações na renda dos clientes.\n", + "O erro médio absoluto do modelo é: 1472176.41\n", + "O erro médio quadrático do modelo é: 1315989882764594.5\n", + "A raiz quadrada do erro médio quadrático é: 12704.330739940186\n", + "Acurácia: 0.65\n", + "\n", + "Veja o comportamento dos resíduos:\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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\n" + }, + "metadata": { + "needs_background": "light" + } + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "---------------------------\n", + "\n", + "Resultados do Polinomial de Grau: 5\n", + "\n", + "Resultado do conjunto de treino - Grau 5 :\n", + "As variáveis explicativas do meu modelo explicam 18.85 % das variações na renda dos clientes.\n", + "O erro médio absoluto do modelo é: 12516.47\n", + "O erro médio quadrático do modelo é: 377221553.92\n", + "A raiz quadrada do erro médio quadrático é: 12704.330739940186\n", + "Acurácia: 0.75\n", + "\n", + "Veja o comportamento dos resíduos:\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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\n" + }, + "metadata": { + "needs_background": "light" + } + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "\n", + "Resultado do conjunto de teste - Grau 5 :\n", + "As variáveis explicativas do meu modelo explicam -13816964.21 % das variações na renda dos clientes.\n", + "O erro médio absoluto do modelo é: 1716501.42\n", + "O erro médio quadrático do modelo é: 63858942879285.67\n", + "A raiz quadrada do erro médio quadrático é: 12704.330739940186\n", + "Acurácia: 0.65\n", + "\n", + "Veja o comportamento dos resíduos:\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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\n" + }, + "metadata": { + "needs_background": "light" + } + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "---------------------------\n", + "\n", + "Resultados do Polinomial de Grau: 6\n", + "\n", + "Resultado do conjunto de treino - Grau 6 :\n", + "As variáveis explicativas do meu modelo explicam 59.23 % das variações na renda dos clientes.\n", + "O erro médio absoluto do modelo é: 7858.94\n", + "O erro médio quadrático do modelo é: 189490829.76\n", + "A raiz quadrada do erro médio quadrático é: 12704.330739940186\n", + "Acurácia: 0.75\n", + "\n", + "Veja o comportamento dos resíduos:\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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\n" + }, + "metadata": { + "needs_background": "light" + } + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "\n", + "Resultado do conjunto de teste - Grau 6 :\n", + "As variáveis explicativas do meu modelo explicam -100390904749.28 % das variações na renda dos clientes.\n", + "O erro médio absoluto do modelo é: 130211783.26\n", + "O erro médio quadrático do modelo é: 4.6398112934485344e+17\n", + "A raiz quadrada do erro médio quadrático é: 12704.330739940186\n", + "Acurácia: 0.65\n", + "\n", + "Veja o comportamento dos resíduos:\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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\n" + }, + "metadata": { + "needs_background": "light" + } + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "---------------------------\n", + "\n", + "Resultados do Polinomial de Grau: 7\n", + "\n", + "Resultado do conjunto de treino - Grau 7 :\n", + "As variáveis explicativas do meu modelo explicam 6.56 % das variações na renda dos clientes.\n", + "O erro médio absoluto do modelo é: 11323.96\n", + "O erro médio quadrático do modelo é: 434355069.57\n", + "A raiz quadrada do erro médio quadrático é: 12704.330739940186\n", + "Acurácia: 0.75\n", + "\n", + "Veja o comportamento dos resíduos:\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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\n" + }, + "metadata": { + "needs_background": "light" + } + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "\n", + "Resultado do conjunto de teste - Grau 7 :\n", + "As variáveis explicativas do meu modelo explicam -1088037024738.78 % das variações na renda dos clientes.\n", + "O erro médio absoluto do modelo é: 401499595.75\n", + "O erro médio quadrático do modelo é: 5.028629319674215e+18\n", + "A raiz quadrada do erro médio quadrático é: 12704.330739940186\n", + "Acurácia: 0.65\n", + "\n", + "Veja o comportamento dos resíduos:\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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\n" + }, + "metadata": { + "needs_background": "light" + } + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "---------------------------\n", + "\n", + "Resultados do Polinomial de Grau: 8\n", + "\n", + "Resultado do conjunto de treino - Grau 8 :\n", + "As variáveis explicativas do meu modelo explicam -19.65 % das variações na renda dos clientes.\n", + "O erro médio absoluto do modelo é: 13498.63\n", + "O erro médio quadrático do modelo é: 556149206.51\n", + "A raiz quadrada do erro médio quadrático é: 12704.330739940186\n", + "Acurácia: 0.75\n", + "\n", + "Veja o comportamento dos resíduos:\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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\n" + }, + "metadata": { + "needs_background": "light" + } + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "\n", + "Resultado do conjunto de teste - Grau 8 :\n", + "As variáveis explicativas do meu modelo explicam -619623100123.76 % das variações na renda dos clientes.\n", + "O erro médio absoluto do modelo é: 335314643.48\n", + "O erro médio quadrático do modelo é: 2.8637397605052314e+18\n", + "A raiz quadrada do erro médio quadrático é: 12704.330739940186\n", + "Acurácia: 0.65\n", + "\n", + "Veja o comportamento dos resíduos:\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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\n" + }, + "metadata": { + "needs_background": "light" + } + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "---------------------------\n", + "\n", + "Resultados do Polinomial de Grau: 9\n", + "\n", + "Resultado do conjunto de treino - Grau 9 :\n", + "As variáveis explicativas do meu modelo explicam -123.35 % das variações na renda dos clientes.\n", + "O erro médio absoluto do modelo é: 18145.33\n", + "O erro médio quadrático do modelo é: 1038203340.48\n", + "A raiz quadrada do erro médio quadrático é: 12704.330739940186\n", + "Acurácia: 0.75\n", + "\n", + "Veja o comportamento dos resíduos:\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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\n" + }, + "metadata": { + "needs_background": "light" + } + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "\n", + "Resultado do conjunto de teste - Grau 9 :\n", + "As variáveis explicativas do meu modelo explicam -973903563350.57 % das variações na renda dos clientes.\n", + "O erro médio absoluto do modelo é: 346495165.35\n", + "O erro médio quadrático do modelo é: 4.5011336029014753e+18\n", + "A raiz quadrada do erro médio quadrático é: 12704.330739940186\n", + "Acurácia: 0.65\n", + "\n", + "Veja o comportamento dos resíduos:\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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\n" + }, + "metadata": { + "needs_background": "light" + } + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "---------------------------\n", + "\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "**Salvando o melhor modelo com o pickle** --> TIRAR DÚVIDA NA AULA SOBRE COMO SALVAR O MELHOR MODELO COM POLINOMIAL " + ], + "metadata": { + "id": "7JK6t95c-c5t" + } + }, + { + "cell_type": "markdown", + "source": [ + "\n", + "ERRADO\n", + "```\n", + "import pickle\n", + "with open ('Reg.pkl','wb') as modelo:\n", + " pickle.dump(pf,modelo)\n", + "\n", + " with open ('Reg.pkl','rb') as modelo:\n", + " regressao=pickle.load(modelo)\n", + "```\n", + "\n" + ], + "metadata": { + "id": "Jax6x5eZoQu1" + } + }, + { + "cell_type": "markdown", + "source": [ + "###### **Substituindo os valores**" + ], + "metadata": { + "id": "mDWrFgTGA8Kr" + } + }, + { + "cell_type": "markdown", + "source": [ + "Agora que os salários foram estimados, vamos substituir os nulos da variável \"income\" na tabela df_2 pelos valores estimados no melhor modelo (polinomial de grau 2)." + ], + "metadata": { + "id": "KrNOCvqK4rnA" + } + }, + { + "cell_type": "code", + "source": [ + "df_5=df_2[df_2['Income'].isnull()]\n", + "X_incnulo=df_5[['Kidhome',\n", + "'MntWines',\n", + "'MntFruits',\n", + "'MntFishProducts', \n", + "'MntSweetProducts',\n", + "'NumCatalogPurchases',\n", + "'NumStorePurchases',\n", + "'NumWebVisitsMonth']]" + ], + "metadata": { + "id": "GtY3xpmMnO76" + }, + "execution_count": 124, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "Pipeline_Regressao(X_incnulo, df_5['Income'], 2)" + ], + "metadata": { + "id": "-YKxI8k5__zW", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 937 + }, + "outputId": "f14d839d-85fa-4123-f0f5-d5425ef63206" + }, + "execution_count": 125, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Resultados do Polinomial de Grau: 2\n", + "\n", + "Resultado do conjunto de treino - Grau 2 :\n", + "As variáveis explicativas do meu modelo explicam 81.69 % das variações na renda dos clientes.\n", + "O erro médio absoluto do modelo é: 6953.15\n", + "O erro médio quadrático do modelo é: 85098902.1\n", + "A raiz quadrada do erro médio quadrático é: 12704.330739940186\n", + "Acurácia: 0.75\n", + "\n", + "Veja o comportamento dos resíduos:\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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\n" + }, + "metadata": { + "needs_background": "light" + } + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "\n", + "Resultado do conjunto de teste - Grau 2 :\n", + "As variáveis explicativas do meu modelo explicam 65.3 % das variações na renda dos clientes.\n", + "O erro médio absoluto do modelo é: 8150.09\n", + "O erro médio quadrático do modelo é: 160362399.84\n", + "A raiz quadrada do erro médio quadrático é: 12704.330739940186\n", + "Acurácia: 0.65\n", + "\n", + "Veja o comportamento dos resíduos:\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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+ }, + "metadata": { + "needs_background": "light" + } + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "---------------------------\n", + "\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "Yhat_final=lr.predict(X_incnulo)" + ], + "metadata": { + "id": "mrfnqO0fnoSH" + }, + "execution_count": 126, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "df_5[['Income_pred']]=Yhat_final.round(2)" + ], + "metadata": { + "id": "kcemLAHfM-p0", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "e730b58a-e1a8-4e2e-87b7-4afedb3df1b4" + }, + "execution_count": 127, + "outputs": [ + { + "output_type": "stream", + "name": "stderr", + "text": [ + "/usr/local/lib/python3.7/dist-packages/pandas/core/frame.py:3678: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " self[col] = igetitem(value, i)\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "df_5.head()" + ], + "metadata": { + "id": "48d7KVXiT-Xs", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 299 + }, + "outputId": "a705fcb9-e780-4dce-d8f7-17076390ee1f" + }, + "execution_count": 128, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + " ID Year_Birth Education Marital_Status Income Kidhome Teenhome \\\n", + "10 1994 1983.0 Graduation Married NaN 1 0 \n", + "27 5255 1986.0 Graduation Single NaN 1 0 \n", + "43 7281 1959.0 PhD Single NaN 0 0 \n", + "48 7244 1951.0 Graduation Single NaN 2 1 \n", + "58 8557 1982.0 Graduation Single NaN 1 0 \n", + "\n", + " Dt_Customer Recency MntWines ... Ec_Divorced Ec_Married Ec_Single \\\n", + "10 2013-11-15 11 5 ... 0 1 0 \n", + "27 2013-02-20 19 5 ... 0 0 1 \n", + "43 2013-11-05 80 81 ... 0 0 1 \n", + "48 2014-01-01 96 48 ... 0 0 1 \n", + "58 2013-06-17 57 11 ... 0 0 1 \n", + "\n", + " Ec_Together Ec_Widow Ed_Basic Ed_Graduation Ed_Master Ed_PhD \\\n", + "10 0 0 0 1 0 0 \n", + "27 0 0 0 1 0 0 \n", + "43 0 0 0 0 0 1 \n", + "48 0 0 0 1 0 0 \n", + "58 0 0 0 1 0 0 \n", + "\n", + " Income_pred \n", + "10 31581.92 \n", + "27 57610.03 \n", + "43 55877.57 \n", + "48 37756.82 \n", + "58 35331.01 \n", + "\n", + "[5 rows x 37 columns]" + ], + "text/html": [ + "\n", + "
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\n", + " " + ] + }, + "metadata": {}, + "execution_count": 128 + } + ] + }, + { + "cell_type": "code", + "source": [ + "df_2['Income'].fillna(df_5['Income_pred'],inplace=True)" + ], + "metadata": { + "id": "GJueZdCGUVAK" + }, + "execution_count": 129, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "df_2.isnull().sum()" + ], + "metadata": { + "id": "n9skr02PYnt2", + "outputId": "554bac38-620b-4d63-de6d-ce40ad2dc892", + "colab": { + "base_uri": "https://localhost:8080/" + } + }, + "execution_count": 130, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "ID 0\n", + "Year_Birth 0\n", + "Education 0\n", + "Marital_Status 0\n", + "Income 0\n", + "Kidhome 0\n", + "Teenhome 0\n", + "Dt_Customer 0\n", + "Recency 0\n", + "MntWines 0\n", + "MntFruits 0\n", + "MntMeatProducts 0\n", + "MntFishProducts 0\n", + "MntSweetProducts 0\n", + "MntGoldProds 0\n", + "NumDealsPurchases 0\n", + "NumWebPurchases 0\n", + "NumCatalogPurchases 0\n", + "NumStorePurchases 0\n", + "NumWebVisitsMonth 0\n", + "AcceptedCmp3 0\n", + "AcceptedCmp4 0\n", + "AcceptedCmp5 0\n", + "AcceptedCmp1 0\n", + "AcceptedCmp2 0\n", + "Complain 0\n", + "Response 0\n", + "Ec_Divorced 0\n", + "Ec_Married 0\n", + "Ec_Single 0\n", + "Ec_Together 0\n", + "Ec_Widow 0\n", + "Ed_Basic 0\n", + "Ed_Graduation 0\n", + "Ed_Master 0\n", + "Ed_PhD 0\n", + "dtype: int64" + ] + }, + "metadata": {}, + "execution_count": 130 + } + ] + }, + { + "cell_type": "code", + "source": [ + "## Com isso, finalizamos a parte de tratamento dos dados (FINALMENTE!)\n", + "## Agora, vamos fazer as análises gráficas e etc p/ ajudar a equipe de marketing na próxima campanha." + ], + "metadata": { + "id": "jBKbiGyleaK9" + }, + "execution_count": 131, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "### **Analisando os dados**" + ], + "metadata": { + "id": "2zIj9La0fUpJ" + } + }, + { + "cell_type": "code", + "source": [ + "## Em primeiro lugar, vamos fazer duas coisas. Relembrar nosso objetivo e visualizar o dataframe \"df\", em que substituimos os dados da renda manualmente. " + ], + "metadata": { + "id": "MkXKBGbSfqn2" + }, + "execution_count": 132, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "**The Objective**\n", + "\n", + "The objective of the team is to build an analysis to address the highest profit for the next direct marketing campaign, scheduled for the next month. The new campaign, sixth, aims at selling a new gadget to the Customer Database. To build the analysis, a pilot campaign involving 2.240 customers was carried out. The customers were selected at random and contacted by phone regarding the acquisition of the gadget. During the following months, customers who bought the\n", + "offer were properly labeled. The total cost of the sample campaign was 6.720MU and the revenue generated by the customers who accepted the offer was 3.674MU. Globally the campaign had a profit of -3.046MU. The success rate of the campaign was 15%." + ], + "metadata": { + "id": "VR4lkPhRf3Ld" + } + }, + { + "cell_type": "code", + "source": [ + "## Revendo o datafreme\n", + "df.info()" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "IoZGO137gKdK", + "outputId": "7848de62-a4f4-4dcc-f01d-ea446bc052ed" + }, + "execution_count": 133, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "\n", + "Int64Index: 2233 entries, 0 to 2239\n", + "Data columns (total 27 columns):\n", + " # Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 ID 2233 non-null int64 \n", + " 1 Year_Birth 2233 non-null float64\n", + " 2 Education 2233 non-null object \n", + " 3 Marital_Status 2233 non-null object \n", + " 4 Income 2233 non-null float64\n", + " 5 Kidhome 2233 non-null int64 \n", + " 6 Teenhome 2233 non-null int64 \n", + " 7 Dt_Customer 2233 non-null object \n", + " 8 Recency 2233 non-null int64 \n", + " 9 MntWines 2233 non-null int64 \n", + " 10 MntFruits 2233 non-null int64 \n", + " 11 MntMeatProducts 2233 non-null int64 \n", + " 12 MntFishProducts 2233 non-null int64 \n", + " 13 MntSweetProducts 2233 non-null int64 \n", + " 14 MntGoldProds 2233 non-null int64 \n", + " 15 NumDealsPurchases 2233 non-null int64 \n", + " 16 NumWebPurchases 2233 non-null int64 \n", + " 17 NumCatalogPurchases 2233 non-null int64 \n", + " 18 NumStorePurchases 2233 non-null int64 \n", + " 19 NumWebVisitsMonth 2233 non-null int64 \n", + " 20 AcceptedCmp3 2233 non-null int64 \n", + " 21 AcceptedCmp4 2233 non-null int64 \n", + " 22 AcceptedCmp5 2233 non-null int64 \n", + " 23 AcceptedCmp1 2233 non-null int64 \n", + " 24 AcceptedCmp2 2233 non-null int64 \n", + " 25 Complain 2233 non-null int64 \n", + " 26 Response 2233 non-null int64 \n", + "dtypes: float64(2), int64(22), object(3)\n", + "memory usage: 488.5+ KB\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "## Vamos criar uma variável de \"idade\" e outra de \"tempo de cliente\" para compor nossa análise\n", + "df['Dt_Customer']=pd.to_datetime(df['Dt_Customer'], format='%Y-%m-%d')" + ], + "metadata": { + "id": "HDlcocOkH3d1" + }, + "execution_count": 134, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "df" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 488 + }, + "id": "exwEjbHqKYH0", + "outputId": "aceb7834-164c-4745-adfb-3dd9be08e481" + }, + "execution_count": 135, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + " ID Year_Birth Education Marital_Status Income Kidhome \\\n", + "0 5524 1957.0 Graduation Single 58138.0 0 \n", + "1 2174 1954.0 Graduation Single 46344.0 1 \n", + "2 4141 1965.0 Graduation Together 71613.0 0 \n", + "3 6182 1984.0 Graduation Together 26646.0 1 \n", + "4 5324 1981.0 PhD Married 58293.0 1 \n", + "... ... ... ... ... ... ... \n", + "2235 10870 1967.0 Graduation Married 61223.0 0 \n", + "2236 4001 1946.0 PhD Together 64014.0 2 \n", + "2237 7270 1981.0 Graduation Divorced 56981.0 0 \n", + "2238 8235 1956.0 Master Together 69245.0 0 \n", + "2239 9405 1954.0 PhD Married 52869.0 1 \n", + "\n", + " Teenhome Dt_Customer Recency MntWines ... 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IDYear_BirthEducationMarital_StatusIncomeKidhomeTeenhomeDt_CustomerRecencyMntWines...NumCatalogPurchasesNumStorePurchasesNumWebVisitsMonthAcceptedCmp3AcceptedCmp4AcceptedCmp5AcceptedCmp1AcceptedCmp2ComplainResponse
055241957.0GraduationSingle58138.0002012-09-0458635...10470000001
121741954.0GraduationSingle46344.0112014-03-083811...1250000000
241411965.0GraduationTogether71613.0002013-08-2126426...21040000000
361821984.0GraduationTogether26646.0102014-02-102611...0460000000
453241981.0PhDMarried58293.0102014-01-1994173...3650000000
..................................................................
2235108701967.0GraduationMarried61223.0012013-06-1346709...3450000000
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223772701981.0GraduationDivorced56981.0002014-01-2591908...31360100000
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1521141946.068.0582PhDSingle82800.0002012-11-24...61230011001
3373731952.062.0608PhDDivorced46610.0022012-10-29...1660000001
3929681943.071.0513PhDDivorced48948.0002013-02-01...10561000001
..................................................................
219387221957.057.0600MasterMarried82347.0002012-11-06...71031001001
219471181957.057.0697GraduationMarried73803.0012012-08-01...5661000001
219826321954.060.0376GraduationMarried50501.0112013-06-18...4461000001
222173661982.032.0360MasterSingle75777.0002013-07-04...61110110001
223994051954.060.0622PhDMarried52869.0112012-10-15...1470000001
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IDYear_BirthAgeCust_forEducationMarital_StatusIncomeKidhomeTeenhomeDt_Customer...NumCatalogPurchasesNumStorePurchasesNumWebVisitsMonthAcceptedCmp3AcceptedCmp4AcceptedCmp5AcceptedCmp1AcceptedCmp2ComplainResponse
121741954.060.0113GraduationSingle46344.0112014-03-08...1250000000
241411965.049.0312GraduationTogether71613.0002013-08-21...21040000000
361821984.030.0139GraduationTogether26646.0102014-02-10...0460000000
453241981.033.0161PhDMarried58293.0102014-01-19...3650000000
574461967.047.0293MasterTogether62513.0012013-09-09...41060000000
..................................................................
223483721974.040.0363GraduationMarried34421.0102013-07-01...0270000000
2235108701967.047.0381GraduationMarried61223.0012013-06-13...3450000000
223640011946.068.019PhDTogether64014.0212014-06-10...2570001000
223772701981.033.0155GraduationDivorced56981.0002014-01-25...31360100000
223882351956.058.0156MasterTogether69245.0012014-01-24...51030000000
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\n", + " " + ] + }, + "metadata": {}, + "execution_count": 145 + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "####**ATENÇÃO: AINDA ESTOU TRABALHANDO NA PARTE ABAIXO**" + ], + "metadata": { + "id": "yHXCS6d37ii5" + } + }, + { + "cell_type": "code", + "source": [ + "## Meu primeiro objetivo é analisar demograficamente, ainda que de forma superficial, as duas populações.\n", + "## Para isso, vamos plotar alguns gráficos para entender melhor ambos os públicos.\n", + "\n", + "fig, axes= plt.subplots(6,2,figsize=[15,30])\n", + "\n", + "## Idade:\n", + "hg_age_r0=sns.histplot(x='Age',data=df_r0,ax=axes[0,0],stat='percent',kde=True,color='#8B4500')\n", + "hg_age_r0.set_title('Idade (R=0)', fontsize=12)\n", + "hg_age_r1=sns.histplot(x='Age',data=df_r1,ax=axes[0,1],stat='percent',kde=True,color='#FF8247')\n", + "hg_age_r1.set_title('Idade (R=1)', fontsize=12)\n", + "\n", + "## Educação:\n", + "hg_ed_r0=sns.histplot(x='Education',data=df_r0,ax=axes[1,0],stat='percent',kde=True,color='#104E8B')\n", + "hg_ed_r0.set_title('Educação (R=0)', fontsize=12)\n", + "hg_ed_r1=sns.histplot(x='Education',data=df_r1,ax=axes[1,1],stat='percent',kde=True,color='Skyblue') ##\t#87CEFF\n", + "hg_ed_r1.set_title('Educação (R=1)', fontsize=12)\n", + "\n", + "## Estado Civil:\n", + "hg_ec_r0=sns.histplot(x='Marital_Status',data=df_r0,ax=axes[2,0],stat='percent',kde=True,color='#FFD700')\n", + "hg_ec_r0.set_title('Estado Civil (R=0)', fontsize=12)\n", + "hg_ec_r1=sns.histplot(x='Marital_Status',data=df_r1,ax=axes[2,1],stat='percent',kde=True,color='#FFF68F') ##\t#87CEFF\n", + "hg_ec_r1.set_title('Estado Civil (R=1)', fontsize=12)\n", + "\n", + "## Renda:\n", + "hg_inc_r0=sns.histplot(x='Income',data=df_r0,ax=axes[3,0],stat='percent',kde=True,color='#00CD66')\n", + "hg_inc_r0.set_title('Renda (R=0)', fontsize=12)\n", + "hg_inc_r1=sns.histplot(x='Income',data=df_r1,ax=axes[3,1],stat='percent',kde=True,color='#00FA9A') ##\t#87CEFF\n", + "hg_inc_r1.set_title('Renda (R=1)', fontsize=12)\n", + "\n", + "## Crianças:\n", + "hg_kid_r0=sns.histplot(x='Kidhome',data=df_r0,ax=axes[4,0],stat='percent',kde=True,color='#8B4789')\n", + "hg_kid_r0.set_title('Crianças em Casa (R=0)', fontsize=12)\n", + "hg_kid_r1=sns.histplot(x='Kidhome',data=df_r1,ax=axes[4,1],stat='percent',kde=True,color='#FF83FA') ##\t#87CEFF\n", + "hg_kid_r1.set_title('Crianças em Casa (R=1)', fontsize=12)\n", + "\n", + "## Adolescentes:\n", + "hg_teen_r0=sns.histplot(x='Teenhome',data=df_r0,ax=axes[5,0],stat='percent',kde=True,color='#8B8682')\n", + "hg_teen_r0.set_title('Adolescentes em Casa (R=0)', fontsize=12)\n", + "hg_teen_r1=sns.histplot(x='Teenhome',data=df_r1,ax=axes[5,1],stat='percent',kde=True,color='#FFF5EE') ##\t#87CEFF\n", + "hg_teen_r1.set_title('Adolescentes em Casa (R=1)', fontsize=12)\n", + "\n", + "##\n", + "\n", + "fig.suptitle('Histogramas de Distribuição Demográfica das Duas Populações', fontsize=20,x=0.5,y=1.03)\n", + "fig.tight_layout()\n", + "\n", + "plt.show()\n" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000 + }, + "id": "BxikQb6T7pHi", + "outputId": "1abd8563-4195-427a-dfac-2596302c15d2" + }, + "execution_count": 146, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
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+ }, + "metadata": { + "needs_background": "light" + } + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "**Conclusões inicais da análise demográfica:**\n", + "\n", + "1. Os clientes que adquiriram o produto têm entre 18 e 71 anos. Porém, mais de 90% deles têm entre 24 e 66 anos e cerca de 75% entre 29 e 61 anos.\n", + "\n", + "\n", + "2. Mais de 95% deles possui renda entre 18.000 e 96.000 UM e mais de 85% entre 28 mil e 96 mil UM.\n", + "\n", + "3. A respeito do nível de escolaridade, aproximadamente 100% dos clientes que adquiriram o produto tem nível de pós-graduação, mestrado ou PhD, sendo que mais de 70% são pós-graduados ou possuem PhD.\n", + "\n", + "\n", + "4. Sobre o estado civil, mais de 90% dos clientes que adquiriram o produto não são viúvos, sendo que aproximamente 60% deles são solteiros ou casados e 30% divorciados ou em união estável.\n", + "\n", + "5. Sobre filhos, cerca de 65% dos clientes que adquiriram o produto não tem crianças em casa e cerca de 34% deles tem apenas uma. Sobre adolescentes, cerca de 70% dos clientes não tem nenhum em casa e 28% tem apenas um.\n", + "\n", + "6. Analisando os gráficos do grupo que não adquiriu o produto, podemos observar que os que adquiriram tem algumas tendências semelhantes, mas outras características bem divergentes, principalmente em relaçao à faixa de renda (1.000-160.000) e a composição do estado civil, como veremos mais especificamente abaixo. " + ], + "metadata": { + "id": "EY2cc3f-QUkP" + } + }, + { + "cell_type": "code", + "source": [ + "## Agora vamos complementar esses gráficos com uma análise percentual mais precisa" + ], + "metadata": { + "id": "KSDe-MrXpGoD" + }, + "execution_count": 147, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "df_r1['Education'].value_counts(normalize=True).round(4)*100" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "C6VX31EeNAsp", + "outputId": "04dbce78-24f9-4c5c-fe52-9cd93a385030" + }, + "execution_count": 148, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "Graduation 45.92\n", + "PhD 29.61\n", + "Master 23.87\n", + "Basic 0.60\n", + "Name: Education, dtype: float64" + ] + }, + "metadata": {}, + "execution_count": 148 + } + ] + }, + { + "cell_type": "code", + "source": [ + "df_r1['Marital_Status'].value_counts(normalize=True).round(4)*100" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "GRS_R7JwNUr4", + "outputId": "3cd34133-c8ca-4c56-b7a0-d771c71569ae" + }, + "execution_count": 149, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "Single 32.33\n", + "Married 29.61\n", + "Together 18.13\n", + "Divorced 14.20\n", + "Widow 5.74\n", + "Name: Marital_Status, dtype: float64" + ] + }, + "metadata": {}, + "execution_count": 149 + } + ] + }, + { + "cell_type": "code", + "source": [ + "## No caso dos clientes que adquiriram o produto, em termos de variáveis categóricas, podemos observar que:\n", + "## Mais de 99% dos clientes ou tem graduação (45,92%), ou PhD (29,6%) ou mestrado (23,87%) completo.\n", + "## Além disso, mais de 75% deles são solteiros (32,33%), casados (29,61%) ou estão em situação de união estável (18,13%). Mas um percentual não desprezível (14.20%) está divorciado." + ], + "metadata": { + "id": "446Wk6E_Nsph" + }, + "execution_count": 150, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "## Comparando essa análise com a mesma para os clientes que não adquiriram o produto, temos:\n", + "df_r0['Education'].value_counts(normalize=True).round(4)*100" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "Rc43hHQPP-qZ", + "outputId": "adc4c4d3-190f-4b94-b824-6a023897d438" + }, + "execution_count": 151, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "Graduation 51.21\n", + "Master 25.92\n", + "PhD 20.14\n", + "Basic 2.73\n", + "Name: Education, dtype: float64" + ] + }, + "metadata": {}, + "execution_count": 151 + } + ] + }, + { + "cell_type": "code", + "source": [ + "df_r0['Marital_Status'].value_counts(normalize=True).round(4)*100" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "VHBWHBGfQK5_", + "outputId": "2dcd1b16-253b-44d3-9222-1d2e795c35b2" + }, + "execution_count": 152, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "Married 40.27\n", + "Together 27.34\n", + "Single 19.66\n", + "Divorced 9.67\n", + "Widow 3.05\n", + "Name: Marital_Status, dtype: float64" + ] + }, + "metadata": {}, + "execution_count": 152 + } + ] + }, + { + "cell_type": "code", + "source": [ + "## As conclusões são um pouco parecidas inicialmente, mas podemos observar algumas diferenças.\n", + "\n", + "## O grupo de graduados, mestres e PhDs representa 97% dos clientes, percentual superior aos 90% do outro grupo, mas com uma diferença na composição relevante: \n", + "## A parcela de clientes PhDs é de 20,1% (ou aprox. -9%) e a de graduados é de (51,2% ou aprox +6%).\n", + "\n", + "## Em termos de estado civil, este grupo contém um percentual muito maior de casados (40,27% ou ~+11%) e de pessoas em união estável (27,34% ou ~+9%) do que o anterior.\n", + "## Por outro lado, o percentual de solteiros dessa amostra foi bem menor (19,66% ou -~13%). \n", + "## Juntos, esses 3 primeiros grupos representam mais de 85% da amostra, 10% a mais do que o caso anterior.\n" + ], + "metadata": { + "id": "CY8E0tleQRqZ" + }, + "execution_count": 153, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "## Agora que entendemos sobre a composição demográfica, vamos aprofundar algumas análises.\n", + "## Será que os clientes com diferente escolaridade tiveram uma taxa de adesão muito diferente ao produto da 6ª campanha?\n", + "\n", + "pd.crosstab(df['Response'],df['Education'])" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 143 + }, + "id": "ELwAtzApjrdq", + "outputId": "ae136346-c024-4a48-c416-ddf6705d0fd9" + }, + "execution_count": 154, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "Education Basic Graduation Master PhD\n", + "Response \n", + "0 52 974 493 383\n", + "1 2 152 79 98" + ], + "text/html": [ + "\n", + "
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\n", + " " + ] + }, + "metadata": {}, + "execution_count": 154 + } + ] + }, + { + "cell_type": "code", + "source": [ + "## Vamos gerar uma visualização da taxa de adesão de cada grau de escolaridade.\n", + "\n", + "## Criando as taxas de cada grau com base nos dados do crosstab\n", + "Ad_Ed_B=((2/(2+52))*100)\n", + "Ad_Ed_G=((152/(152+974))*100)\n", + "Ad_Ed_M=((79/(79+493))*100)\n", + "Ad_Ed_P=((98/(98+383))*100)\n", + "\n", + "## Criando o D.F.\n", + "Ad_Educ=pd.DataFrame({\"Taxa de Adesão\": [Ad_Ed_B, Ad_Ed_G, Ad_Ed_M, Ad_Ed_P]}, \n", + "index=[\"Basic\", \"Graduation\", \"Master\", \"PhD\"])\n", + "\n", + "## Criando a visualização\n", + "plot1=Ad_Educ.plot(kind=\"bar\")\n", + "plot1.set_title('Taxa de Adesão Por Escolaridade', fontsize=12,)\n", + "\n", + "plt.show()" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 327 + }, + "id": "cQct42Z6nZyx", + "outputId": "27c48ff2-be58-4551-d0d3-d5eb72e29b66" + }, + "execution_count": 155, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
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\n" + }, + "metadata": { + "needs_background": "light" + } + } + ] + }, + { + "cell_type": "code", + "source": [ + "## E quando analisamos a mesma coisa pelo estado civil?\n", + "\n", + "pd.crosstab(df['Response'],df['Marital_Status'])" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 143 + }, + "id": "JwIlViLFFSPf", + "outputId": "d679b7b6-8892-43f9-fdfb-4daa4a6d2a0b" + }, + "execution_count": 156, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "Marital_Status Divorced Married Single Together Widow\n", + "Response \n", + "0 184 766 374 520 58\n", + "1 47 98 107 60 19" + ], + "text/html": [ + "\n", + "
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\n", + " " + ] + }, + "metadata": {}, + "execution_count": 156 + } + ] + }, + { + "cell_type": "code", + "source": [ + "## Vamos gerar uma visualização da taxa de adesão de cada estado civil.\n", + "\n", + "## Criando as taxas de cada grau com base nos dados do crosstab\n", + "Ad_M_S=((107/(107+374))*100)\n", + "Ad_M_T=((60/(60+520))*100)\n", + "Ad_M_M=((98/(98+766))*100)\n", + "Ad_M_D=((47/(47+184))*100)\n", + "Ad_M_W=((19/(19+58))*100)\n", + "\n", + "## Criando o D.F.\n", + "Ad_MS=pd.DataFrame({\"Taxa de Adesão\": [Ad_M_S, Ad_M_T, Ad_M_M, Ad_M_D, Ad_M_W]}, \n", + "index=[\"Single\", \"Together\", \"Married\", \"Divorced\", 'Widow'])\n", + "\n", + "## Criando a visualização\n", + "plot1=Ad_MS.plot(kind=\"bar\",color='#DC143C')\n", + "plot1.set_title('Taxa de Adesão Por Escolaridade', fontsize=12,)\n", + "\n", + "plt.show()" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 316 + }, + "id": "GwNi-zjPE9xt", + "outputId": "fc5ef29d-2baa-4a02-f102-fcaaa3e8222e" + }, + "execution_count": 157, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
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+ }, + "metadata": { + "needs_background": "light" + } + } + ] + }, + { + "cell_type": "code", + "source": [ + "## A taxa de adesão muda bastante conforme as categorias de escolaridade e estado civil.\n", + "## Vamos ver se a correlação é capaz de capturar isso também. " + ], + "metadata": { + "id": "gGNTHt2YuaGx" + }, + "execution_count": 158, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "df_3[['Education','Response']].corr().round(2)" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 112 + }, + "id": "KF9UzlNYs_By", + "outputId": "05bab0f4-7d94-49b0-afa2-40a768f919ce" + }, + "execution_count": 159, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + " Education Response\n", + "Education 1.00 0.08\n", + "Response 0.08 1.00" + ], + "text/html": [ + "\n", + "
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Ec_SingleEc_TogetherEc_MarriedEc_DivorcedEc_WidowResponse
Ec_Single1.00-0.31-0.42-0.18-0.100.11
Ec_Together-0.311.00-0.47-0.20-0.11-0.07
Ec_Married-0.42-0.471.00-0.27-0.15-0.08
Ec_Divorced-0.18-0.20-0.271.00-0.060.05
Ec_Widow-0.10-0.11-0.15-0.061.000.05
Response0.11-0.07-0.080.050.051.00
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\n", + " " + ] + }, + "metadata": {}, + "execution_count": 160 + } + ] + }, + { + "cell_type": "code", + "source": [ + "## Em ambos os casos, a correlação foi baixa para todas as variáveis." + ], + "metadata": { + "id": "WogKC12Ju0M7" + }, + "execution_count": 161, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "## Agora, vamos aprofundar mais um pouco na análise incluindo também hábitos de consumo.\n", + "## Para isso, vamos plotar alguns gráficos.\n", + "\n", + "janela, graficos= plt.subplots(3,2,figsize=[15,20])\n", + "\n", + "## Educação, Renda e Estado Civil:\n", + "sns.boxplot(x='Income',y='Education',hue='Marital_Status',data=df_r0,order=['Basic', 'Graduation', 'Master', 'PhD'],hue_order=['Single','Together','Married','Divorced','Widow'],ax=graficos[0,0],palette='mako').set_title('Renda Por Escolaridade e Estado Civil (R=0)', fontsize=12)\n", + "sns.boxplot(x='Income',y='Education',hue='Marital_Status',data=df_r1,order=['Basic', 'Graduation', 'Master', 'PhD'],hue_order=['Single','Together','Married','Divorced','Widow'],ax=graficos[0,1],palette='mako').set_title('Renda Por Escolaridade e Estado Civil (R=1)', fontsize=12)\n", + "\n", + "## Total de compras:\n", + "sns.boxplot(x='NumPurchases',y='Education',hue='Marital_Status',data=df_r0,order=['Basic', 'Graduation', 'Master', 'PhD'],hue_order=['Single','Together','Married','Divorced','Widow'],ax=graficos[1,0],palette='bright').set_title('Compras Por Escolaridade e Estado Civil (R=0)', fontsize=12)\n", + "sns.boxplot(x='NumPurchases',y='Education',hue='Marital_Status',data=df_r1,order=['Basic', 'Graduation', 'Master', 'PhD'],hue_order=['Single','Together','Married','Divorced','Widow'],ax=graficos[1,1],palette='bright').set_title('Compras Por Escolaridade e Estado Civil (R=1)', fontsize=12)\n", + "\n", + "## Consumo Total\n", + "sns.boxplot(x='MntTotal',y='Education',hue='Marital_Status',data=df_r0,order=['Basic', 'Graduation', 'Master', 'PhD'],hue_order=['Single','Together','Married','Divorced','Widow'],ax=graficos[2,0],palette='pastel').set_title('Consumo Total Por Escolaridade e Estado Civil (R=0)', fontsize=12)\n", + "sns.boxplot(x='MntTotal',y='Education',hue='Marital_Status',data=df_r1,order=['Basic', 'Graduation', 'Master', 'PhD'],hue_order=['Single','Together','Married','Divorced','Widow'],ax=graficos[2,1],palette='pastel').set_title('Consumo Total Por Escolaridade e Estado Civil (R=1)', fontsize=12)\n", + "\n", + "## Configurações finais do plot\n", + "janela.suptitle('BoxPlots de Características Demográficas', fontsize=20,x=0.5,y=1.03)\n", + "janela.tight_layout()\n", + "\n", + "plt.show()" + ], + "metadata": { + "id": "KC2Vm8u87pEJ", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000 + }, + "outputId": "99c78fa7-bd22-4e30-f298-c4ff92b1dee1" + }, + "execution_count": 162, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
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\n" + }, + "metadata": { + "needs_background": "light" + } + } + ] + }, + { + "cell_type": "code", + "source": [], + "metadata": { + "id": "jMiodTrbO8Aw" + }, + "execution_count": 162, + "outputs": [] + }, + { + "cell_type": "code", + "source": [], + "metadata": { + "id": "ColCoAMFDW9G" + }, + "execution_count": 162, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "##### **IDEIA AINDA EM FORMULAÇÃO**: INSERIR PARTE ALTERNATIVA COM AUTOMAÇÃO DO PANDAS PROFILING" + ], + "metadata": { + "id": "P8UEUtsCEdqz" + } + }, + { + "cell_type": "code", + "source": [ + "!pip install https://github.com/pandas-profiling/pandas-profiling/archive/master.zip" + ], + "metadata": { + "id": "7QuHfW3ixZNj", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "a3292f98-a532-41fe-964f-d32fab3fab5e" + }, + "execution_count": 163, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n", + "Collecting https://github.com/pandas-profiling/pandas-profiling/archive/master.zip\n", + " Downloading https://github.com/pandas-profiling/pandas-profiling/archive/master.zip (21.9 MB)\n", + "\u001b[K |████████████████████████████████| 21.9 MB 1.4 MB/s \n", + "\u001b[?25hRequirement already satisfied: joblib~=1.1.0 in /usr/local/lib/python3.7/dist-packages (from pandas-profiling==3.2.0) (1.1.0)\n", + "Requirement already satisfied: scipy>=1.4.1 in /usr/local/lib/python3.7/dist-packages (from pandas-profiling==3.2.0) (1.7.3)\n", + "Requirement already satisfied: pandas!=1.0.0,!=1.0.1,!=1.0.2,!=1.1.0,>=0.25.3 in /usr/local/lib/python3.7/dist-packages (from pandas-profiling==3.2.0) (1.3.5)\n", + "Requirement already satisfied: matplotlib>=3.2.0 in /usr/local/lib/python3.7/dist-packages (from pandas-profiling==3.2.0) (3.2.2)\n", + "Requirement already satisfied: pydantic>=1.8.1 in /usr/local/lib/python3.7/dist-packages (from pandas-profiling==3.2.0) (1.9.2)\n", + "Requirement already satisfied: PyYAML>=5.0.0 in /usr/local/lib/python3.7/dist-packages (from pandas-profiling==3.2.0) (6.0)\n", + "Requirement already satisfied: jinja2>=2.11.1 in /usr/local/lib/python3.7/dist-packages (from pandas-profiling==3.2.0) (2.11.3)\n", + "Collecting visions[type_image_path]==0.7.5\n", + " Downloading visions-0.7.5-py3-none-any.whl (102 kB)\n", + "\u001b[K |████████████████████████████████| 102 kB 28.2 MB/s \n", + "\u001b[?25hRequirement already satisfied: numpy>=1.16.0 in /usr/local/lib/python3.7/dist-packages (from pandas-profiling==3.2.0) (1.21.6)\n", + "Collecting htmlmin>=0.1.12\n", + " Downloading htmlmin-0.1.12.tar.gz (19 kB)\n", + "Requirement already satisfied: missingno>=0.4.2 in /usr/local/lib/python3.7/dist-packages (from pandas-profiling==3.2.0) (0.5.1)\n", + "Collecting phik>=0.11.1\n", + " Downloading phik-0.12.2-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (690 kB)\n", + "\u001b[K |████████████████████████████████| 690 kB 49.0 MB/s \n", + "\u001b[?25hCollecting tangled-up-in-unicode==0.2.0\n", + " Downloading tangled_up_in_unicode-0.2.0-py3-none-any.whl (4.7 MB)\n", + "\u001b[K |████████████████████████████████| 4.7 MB 42.8 MB/s \n", + "\u001b[?25hCollecting requests>=2.24.0\n", + " Downloading requests-2.28.1-py3-none-any.whl (62 kB)\n", + "\u001b[K |████████████████████████████████| 62 kB 1.4 MB/s \n", + "\u001b[?25hRequirement already satisfied: tqdm>=4.48.2 in /usr/local/lib/python3.7/dist-packages (from pandas-profiling==3.2.0) (4.64.0)\n", + "Requirement already satisfied: seaborn>=0.10.1 in /usr/local/lib/python3.7/dist-packages (from pandas-profiling==3.2.0) (0.11.2)\n", + "Collecting multimethod>=1.4\n", + " Downloading multimethod-1.8-py3-none-any.whl (9.8 kB)\n", + "Requirement already satisfied: networkx>=2.4 in /usr/local/lib/python3.7/dist-packages (from visions[type_image_path]==0.7.5->pandas-profiling==3.2.0) (2.6.3)\n", + "Requirement already satisfied: attrs>=19.3.0 in /usr/local/lib/python3.7/dist-packages (from visions[type_image_path]==0.7.5->pandas-profiling==3.2.0) (22.1.0)\n", + "Collecting imagehash\n", + " Downloading ImageHash-4.2.1.tar.gz (812 kB)\n", + "\u001b[K |████████████████████████████████| 812 kB 67.4 MB/s \n", + "\u001b[?25hRequirement already satisfied: Pillow in /usr/local/lib/python3.7/dist-packages (from visions[type_image_path]==0.7.5->pandas-profiling==3.2.0) (7.1.2)\n", + "Requirement already satisfied: MarkupSafe>=0.23 in /usr/local/lib/python3.7/dist-packages (from jinja2>=2.11.1->pandas-profiling==3.2.0) (2.0.1)\n", + "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.7/dist-packages (from matplotlib>=3.2.0->pandas-profiling==3.2.0) (0.11.0)\n", + "Requirement already satisfied: python-dateutil>=2.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib>=3.2.0->pandas-profiling==3.2.0) (2.8.2)\n", + "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib>=3.2.0->pandas-profiling==3.2.0) (3.0.9)\n", + "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib>=3.2.0->pandas-profiling==3.2.0) (1.4.4)\n", + "Requirement already satisfied: typing-extensions in /usr/local/lib/python3.7/dist-packages (from kiwisolver>=1.0.1->matplotlib>=3.2.0->pandas-profiling==3.2.0) (4.1.1)\n", + "Requirement already satisfied: pytz>=2017.3 in /usr/local/lib/python3.7/dist-packages (from pandas!=1.0.0,!=1.0.1,!=1.0.2,!=1.1.0,>=0.25.3->pandas-profiling==3.2.0) (2022.2.1)\n", + "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.7/dist-packages (from python-dateutil>=2.1->matplotlib>=3.2.0->pandas-profiling==3.2.0) (1.15.0)\n", + "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests>=2.24.0->pandas-profiling==3.2.0) (2022.6.15)\n", + "Requirement already satisfied: charset-normalizer<3,>=2 in /usr/local/lib/python3.7/dist-packages (from requests>=2.24.0->pandas-profiling==3.2.0) (2.1.0)\n", + "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests>=2.24.0->pandas-profiling==3.2.0) (2.10)\n", + "Requirement already satisfied: urllib3<1.27,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests>=2.24.0->pandas-profiling==3.2.0) (1.24.3)\n", + "Requirement already satisfied: PyWavelets in /usr/local/lib/python3.7/dist-packages (from imagehash->visions[type_image_path]==0.7.5->pandas-profiling==3.2.0) (1.3.0)\n", + "Building wheels for collected packages: pandas-profiling, htmlmin, imagehash\n", + " Building wheel for pandas-profiling (setup.py) ... \u001b[?25l\u001b[?25hdone\n", + " Created wheel for pandas-profiling: filename=pandas_profiling-3.2.0-py2.py3-none-any.whl size=261257 sha256=e37d3f2d40d8cefa256431d4bf4ef13b2d3a1fa3ea8f00fbac9c5683e8a57225\n", + " Stored in directory: /tmp/pip-ephem-wheel-cache-3lx7foft/wheels/cc/d5/09/083fb07c9363a2f45854b0e3a7de7d7c560f07da74b9e9769d\n", + " Building wheel for htmlmin (setup.py) ... \u001b[?25l\u001b[?25hdone\n", + " Created wheel for htmlmin: filename=htmlmin-0.1.12-py3-none-any.whl size=27098 sha256=ab427bcba2a6ed18ae55326f398257bb76e2fee5c1ebcebb416a77000ea0d8e3\n", + " Stored in directory: /root/.cache/pip/wheels/70/e1/52/5b14d250ba868768823940c3229e9950d201a26d0bd3ee8655\n", + " Building wheel for imagehash (setup.py) ... \u001b[?25l\u001b[?25hdone\n", + " Created wheel for imagehash: filename=ImageHash-4.2.1-py2.py3-none-any.whl size=295206 sha256=4188663de6130c645d63cab844d5568bdffb51bc094f7a9d674a432fefdf555b\n", + " Stored in directory: /root/.cache/pip/wheels/4c/d5/59/5e3e297533ddb09407769762985d134135064c6831e29a914e\n", + "Successfully built pandas-profiling htmlmin imagehash\n", + "Installing collected packages: tangled-up-in-unicode, multimethod, visions, imagehash, requests, phik, htmlmin, pandas-profiling\n", + " Attempting uninstall: requests\n", + " Found existing installation: requests 2.23.0\n", + " Uninstalling requests-2.23.0:\n", + " Successfully uninstalled requests-2.23.0\n", + " Attempting uninstall: pandas-profiling\n", + " Found existing installation: pandas-profiling 1.4.1\n", + " Uninstalling pandas-profiling-1.4.1:\n", + " Successfully uninstalled pandas-profiling-1.4.1\n", + "Successfully installed htmlmin-0.1.12 imagehash-4.2.1 multimethod-1.8 pandas-profiling-3.2.0 phik-0.12.2 requests-2.28.1 tangled-up-in-unicode-0.2.0 visions-0.7.5\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "from pandas_profiling import ProfileReport" + ], + "metadata": { + "id": "rE9LVlPDwABl" + }, + "execution_count": 164, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "Pr= ProfileReport(df_r1)\n", + "Pr.to_file(output_file='PandasProfiling_v1.html')" + ], + "metadata": { + "id": "CUUMsFS3wLy8" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "Pr" + ], + "metadata": { + "id": "lLDgupU_yorj" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [], + "metadata": { + "id": "QXVKPILwWMiK" + }, + "execution_count": null, + "outputs": [] + } + ] +} \ No newline at end of file From e0cd95dbae3ef6e6b973c09fabccf815a9fd516e Mon Sep 17 00:00:00 2001 From: Marcilio Duarte <104692475+marcilioduarte@users.noreply.github.com> Date: Mon, 23 Jan 2023 00:00:29 -0300 Subject: [PATCH 16/18] Delete Houses_Regression_(Working_Project).ipynb --- Houses_Regression_(Working_Project).ipynb | 5278 --------------------- 1 file changed, 5278 deletions(-) delete mode 100644 Houses_Regression_(Working_Project).ipynb diff --git a/Houses_Regression_(Working_Project).ipynb b/Houses_Regression_(Working_Project).ipynb deleted file mode 100644 index 0afe0fb..0000000 --- a/Houses_Regression_(Working_Project).ipynb +++ /dev/null @@ -1,5278 +0,0 @@ -{ - "nbformat": 4, - "nbformat_minor": 0, - "metadata": { - "colab": { - "name": "Houses_Regression (Working Project)", - "provenance": [], - "authorship_tag": "ABX9TyOXXte6BvmfTOq5KknuTzXV", - "include_colab_link": true - }, - "kernelspec": { - "name": "python3", - "display_name": "Python 3" - }, - "language_info": { - "name": "python" - } - }, - "cells": [ - { - "cell_type": "markdown", - "metadata": { - "id": "view-in-github", - "colab_type": "text" - }, - "source": [ - "\"Open" - ] - }, - { - "cell_type": "markdown", - "source": [ - "##**Como utilizar um modelo de regressão linear para preencher dados nulos do preço de venda de imóveis:**" - ], - "metadata": { - "id": "mcr777QvC6jl" - } - }, - { - "cell_type": "markdown", - "source": [ - "###**Introdução**\n" - ], - "metadata": { - "id": "OTe1lYfqECgZ" - } - }, - { - "cell_type": "markdown", - "source": [ - "Esse projeto trata-se de um exercício de regressão linear da minha especialização na Awari que utilizei como base para construção de portfólio. \n", - "\n", - "Fiz a parte do tratamento dos dados em conjunto com meus colegas de turma @Érika Rocha e @Lucas Castro, mas a partir da etapa de definição das variáveis do modelo de regressão optamos por fazer sozinhos para treinar nossas habilidades. \n", - "\n", - "Os dados nos foram disponibilizados pelo nosso professor, @Anderson Cordeiro, e o objetivo principal do projeto é usar um modelo de regressão linear para estimar e substituir valores nulos do preço de venda dos imóveis da base que não tenham esta informação. " - ], - "metadata": { - "id": "HtczVDg1_4Xf" - } - }, - { - "cell_type": "markdown", - "source": [ - "###***Importando as bibliotecas e unindo os dados***" - ], - "metadata": { - "id": "HQglJjqJxUkE" - } - }, - { - "cell_type": "markdown", - "source": [ - "**Importando as bibliotecas que possivelmente iremos utilizar**" - ], - "metadata": { - "id": "wN1M_ZI5pcAH" - } - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "id": "p9umx0-TwfZ-" - }, - "outputs": [], - "source": [ - "import math\n", - "import statistics\n", - "import numpy as np\n", - "import pandas as pd\n", - "import matplotlib.pyplot as plt\n", - "import seaborn as sns\n", - "from sklearn.linear_model import LinearRegression\n", - "from sklearn.model_selection import train_test_split\n", - "from sklearn.metrics import r2_score, mean_absolute_error, mean_squared_error\n", - "import pickle" - ] - }, - { - "cell_type": "code", - "source": [ - "df_test=pd.read_csv('test.csv')" - ], - "metadata": { - "id": "Gs-UED_jAKgt" - 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\n", - " " - ] - }, - "metadata": {}, - "execution_count": 6 - } - ] - }, - { - "cell_type": "markdown", - "source": [ - "**Repetindo todo o processoa acima para os dados de treino**\n" - ], - "metadata": { - "id": "VQwxCfsmsKoY" - } - }, - { - "cell_type": "code", - "source": [ - "df_train=pd.read_csv('train.csv')" - ], - "metadata": { - "id": "5ZSdyN1nxjcx" - }, - "execution_count": 7, - "outputs": [] - }, - { - "cell_type": "code", - "source": [ - "df_train['istrain']=1\n", - "df_train.head()" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 0 - }, - "id": "mJtt_o1Yy8vH", - "outputId": "47869c0f-9ebc-4c44-a319-fe6c22ce451b" - }, - "execution_count": 8, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - " Id MSSubClass MSZoning LotFrontage LotArea Street Alley LotShape \\\n", - "0 1 60 RL 65.0 8450 Pave NaN Reg \n", - "1 2 20 RL 80.0 9600 Pave NaN Reg \n", - "2 3 60 RL 68.0 11250 Pave NaN IR1 \n", - "3 4 70 RL 60.0 9550 Pave NaN IR1 \n", - "4 5 60 RL 84.0 14260 Pave NaN IR1 \n", - "\n", - " LandContour Utilities ... PoolQC Fence MiscFeature MiscVal MoSold YrSold \\\n", - "0 Lvl AllPub ... NaN NaN NaN 0 2 2008 \n", - "1 Lvl AllPub ... NaN NaN NaN 0 5 2007 \n", - "2 Lvl AllPub ... NaN NaN NaN 0 9 2008 \n", - "3 Lvl AllPub ... NaN NaN NaN 0 2 2006 \n", - "4 Lvl AllPub ... NaN NaN NaN 0 12 2008 \n", - "\n", - " SaleType SaleCondition SalePrice istrain \n", - "0 WD Normal 208500 1 \n", - "1 WD Normal 181500 1 \n", - "2 WD Normal 223500 1 \n", - "3 WD Abnorml 140000 1 \n", - "4 WD Normal 250000 1 \n", - "\n", - "[5 rows x 82 columns]" - ], - "text/html": [ - "\n", - "
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IdMSSubClassMSZoningLotFrontageLotAreaStreetAlleyLotShapeLandContourUtilities...PoolQCFenceMiscFeatureMiscValMoSoldYrSoldSaleTypeSaleConditionSalePriceistrain
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1220RL80.09600PaveNaNRegLvlAllPub...NaNNaNNaN052007WDNormal1815001
2360RL68.011250PaveNaNIR1LvlAllPub...NaNNaNNaN092008WDNormal2235001
3470RL60.09550PaveNaNIR1LvlAllPub...NaNNaNNaN022006WDAbnorml1400001
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\n", - " " - ] - }, - "metadata": {}, - "execution_count": 8 - } - ] - }, - { - "cell_type": "markdown", - "source": [ - "**Juntando as duas tabelas para fazer os demais tratamentos de uma vez só**" - ], - "metadata": { - "id": "9_KnqVD8sTTQ" - } - }, - { - "cell_type": "code", - "source": [ - "df=pd.concat([df_test,df_train],axis=0)\n", - "df.shape" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "W8NIWs8V0042", - "outputId": "bbdf237b-7ef5-451c-c043-85ddbdb1666c" - }, - "execution_count": 9, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "(2919, 82)" - ] - }, - "metadata": {}, - "execution_count": 9 - } - ] - }, - { - "cell_type": "markdown", - "source": [ - "### ***Análise Exploratória e Tratamentos Iniciais***" - ], - "metadata": { - "id": "KXFsKysVtMt6" - } - }, - { - "cell_type": "markdown", - "source": [ - "Tarefas que podem ser executadas nesta base:\n", - "\n", - "\n", - "1. Tratar os nulos e os valores de NAs que estão como nulos, mas que se tratam de categorias em variáveis categóricas.\n", - "2. Verificar se tem alguma variável com valores constantes com o .describe()\n", - "2. Verificar se tem alguma variável com categorias tipo que não correspondem com o esperado com o value_counts ou com o unique;\n", - "3. Criar as dummies ou categorias numéricas que forem necessárias em variáveis categóricas;" - ], - "metadata": { - "id": "nW_3QYQ4tZjX" - } - }, - { - "cell_type": "code", - "source": [ - "df.info()" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "g83LvL7Q1rBr", - "outputId": "cdeeaaa1-5a22-4cf0-8ee1-a77aa3b0bad3" - }, - "execution_count": 10, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "\n", - "Int64Index: 2919 entries, 0 to 1459\n", - "Data columns (total 82 columns):\n", - " # Column Non-Null Count Dtype \n", - "--- ------ -------------- ----- \n", - " 0 Id 2919 non-null int64 \n", - " 1 MSSubClass 2919 non-null int64 \n", - " 2 MSZoning 2915 non-null object \n", - " 3 LotFrontage 2433 non-null float64\n", - " 4 LotArea 2919 non-null int64 \n", - " 5 Street 2919 non-null object \n", - " 6 Alley 198 non-null object \n", - " 7 LotShape 2919 non-null object \n", - " 8 LandContour 2919 non-null object \n", - " 9 Utilities 2917 non-null object \n", - " 10 LotConfig 2919 non-null object \n", - " 11 LandSlope 2919 non-null object \n", - " 12 Neighborhood 2919 non-null object \n", - " 13 Condition1 2919 non-null object \n", - " 14 Condition2 2919 non-null object \n", - " 15 BldgType 2919 non-null object \n", - " 16 HouseStyle 2919 non-null object \n", - " 17 OverallQual 2919 non-null int64 \n", - " 18 OverallCond 2919 non-null int64 \n", - " 19 YearBuilt 2919 non-null int64 \n", - " 20 YearRemodAdd 2919 non-null int64 \n", - " 21 RoofStyle 2919 non-null object \n", - " 22 RoofMatl 2919 non-null object \n", - " 23 Exterior1st 2918 non-null object \n", - " 24 Exterior2nd 2918 non-null object \n", - " 25 MasVnrType 2895 non-null object \n", - " 26 MasVnrArea 2896 non-null float64\n", - " 27 ExterQual 2919 non-null object \n", - " 28 ExterCond 2919 non-null object \n", - " 29 Foundation 2919 non-null object \n", - " 30 BsmtQual 2838 non-null object \n", - " 31 BsmtCond 2837 non-null object \n", - " 32 BsmtExposure 2837 non-null object \n", - " 33 BsmtFinType1 2840 non-null object \n", - " 34 BsmtFinSF1 2918 non-null float64\n", - " 35 BsmtFinType2 2839 non-null object \n", - " 36 BsmtFinSF2 2918 non-null float64\n", - " 37 BsmtUnfSF 2918 non-null float64\n", - " 38 TotalBsmtSF 2918 non-null float64\n", - " 39 Heating 2919 non-null object \n", - " 40 HeatingQC 2919 non-null object \n", - " 41 CentralAir 2919 non-null object \n", - " 42 Electrical 2918 non-null object \n", - " 43 1stFlrSF 2919 non-null int64 \n", - " 44 2ndFlrSF 2919 non-null int64 \n", - " 45 LowQualFinSF 2919 non-null int64 \n", - " 46 GrLivArea 2919 non-null int64 \n", - " 47 BsmtFullBath 2917 non-null float64\n", - " 48 BsmtHalfBath 2917 non-null float64\n", - " 49 FullBath 2919 non-null int64 \n", - " 50 HalfBath 2919 non-null int64 \n", - " 51 BedroomAbvGr 2919 non-null int64 \n", - " 52 KitchenAbvGr 2919 non-null int64 \n", - " 53 KitchenQual 2918 non-null object \n", - " 54 TotRmsAbvGrd 2919 non-null int64 \n", - " 55 Functional 2917 non-null object \n", - " 56 Fireplaces 2919 non-null int64 \n", - " 57 FireplaceQu 1499 non-null object \n", - " 58 GarageType 2762 non-null object \n", - " 59 GarageYrBlt 2760 non-null float64\n", - " 60 GarageFinish 2760 non-null object \n", - " 61 GarageCars 2918 non-null float64\n", - " 62 GarageArea 2918 non-null float64\n", - " 63 GarageQual 2760 non-null object \n", - " 64 GarageCond 2760 non-null object \n", - " 65 PavedDrive 2919 non-null object \n", - " 66 WoodDeckSF 2919 non-null int64 \n", - " 67 OpenPorchSF 2919 non-null int64 \n", - " 68 EnclosedPorch 2919 non-null int64 \n", - " 69 3SsnPorch 2919 non-null int64 \n", - " 70 ScreenPorch 2919 non-null int64 \n", - " 71 PoolArea 2919 non-null int64 \n", - " 72 PoolQC 10 non-null object \n", - " 73 Fence 571 non-null object \n", - " 74 MiscFeature 105 non-null object \n", - " 75 MiscVal 2919 non-null int64 \n", - " 76 MoSold 2919 non-null int64 \n", - " 77 YrSold 2919 non-null int64 \n", - " 78 SaleType 2918 non-null object \n", - " 79 SaleCondition 2919 non-null object \n", - " 80 istrain 2919 non-null int64 \n", - " 81 SalePrice 1460 non-null float64\n", - "dtypes: float64(12), int64(27), object(43)\n", - "memory usage: 1.8+ MB\n" - ] - } - ] - }, - { - "cell_type": "code", - "source": [ - "df.columns" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "4T3zsguQKndv", - "outputId": "2493e260-912d-49fe-8e90-278b00a720ca" - }, - "execution_count": 11, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "Index(['Id', 'MSSubClass', 'MSZoning', 'LotFrontage', 'LotArea', 'Street',\n", - " 'Alley', 'LotShape', 'LandContour', 'Utilities', 'LotConfig',\n", - " 'LandSlope', 'Neighborhood', 'Condition1', 'Condition2', 'BldgType',\n", - " 'HouseStyle', 'OverallQual', 'OverallCond', 'YearBuilt', 'YearRemodAdd',\n", - " 'RoofStyle', 'RoofMatl', 'Exterior1st', 'Exterior2nd', 'MasVnrType',\n", - " 'MasVnrArea', 'ExterQual', 'ExterCond', 'Foundation', 'BsmtQual',\n", - " 'BsmtCond', 'BsmtExposure', 'BsmtFinType1', 'BsmtFinSF1',\n", - " 'BsmtFinType2', 'BsmtFinSF2', 'BsmtUnfSF', 'TotalBsmtSF', 'Heating',\n", - " 'HeatingQC', 'CentralAir', 'Electrical', '1stFlrSF', '2ndFlrSF',\n", - " 'LowQualFinSF', 'GrLivArea', 'BsmtFullBath', 'BsmtHalfBath', 'FullBath',\n", - " 'HalfBath', 'BedroomAbvGr', 'KitchenAbvGr', 'KitchenQual',\n", - " 'TotRmsAbvGrd', 'Functional', 'Fireplaces', 'FireplaceQu', 'GarageType',\n", - " 'GarageYrBlt', 'GarageFinish', 'GarageCars', 'GarageArea', 'GarageQual',\n", - " 'GarageCond', 'PavedDrive', 'WoodDeckSF', 'OpenPorchSF',\n", - " 'EnclosedPorch', '3SsnPorch', 'ScreenPorch', 'PoolArea', 'PoolQC',\n", - " 'Fence', 'MiscFeature', 'MiscVal', 'MoSold', 'YrSold', 'SaleType',\n", - " 'SaleCondition', 'istrain', 'SalePrice'],\n", - " dtype='object')" - ] - }, - "metadata": {}, - "execution_count": 11 - } - ] - }, - { - "cell_type": "markdown", - "source": [ - "#### **Tratando os nulos das categorias em que NA é uma categoria.**" - ], - "metadata": { - "id": "lZn1irwboUO4" - } - }, - { - "cell_type": "code", - "source": [ - "df['Alley'].fillna(\"no_access\",inplace=True)\n", - "df['BsmtQual'].fillna('no_bsmt',inplace=True)\n", - "df['BsmtCond'].fillna('no_bsmt',inplace=True)\n", - "df['BsmtExposure'].fillna('no_bsmt',inplace=True)\n", - "df['BsmtFinType1'].fillna('no_bsmt',inplace=True)\n", - "df['BsmtFinType2'].fillna('no_bsmt',inplace=True)\n", - "df['Fireplaces'].fillna('no_fireplc',inplace=True)\n", - "df['GarageType'].fillna('no_garage',inplace=True)\n", - "df['GarageFinish'].fillna('no_garage',inplace=True)\n", - "df['GarageQual'].fillna('no_garage',inplace=True)\n", - "df['GarageCond'].fillna('no_garage',inplace=True)\n", - "df['PoolQC'].fillna('no_pool',inplace=True)\n", - "df['Fence'].fillna(\"no_fence\",inplace=True)\n", - "df['MiscFeature'].fillna(\"none\",inplace=True)\n" - ], - "metadata": { - "id": "DrCsB0f0Gzj_" - }, - "execution_count": 12, - "outputs": [] - }, - { - "cell_type": "code", - "source": [ - "df['Alley'].value_counts()" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "iLQg4p_-2xyY", - "outputId": "334ff79e-af64-43bc-8211-7cebcd358cb2" - }, - "execution_count": 13, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "no_access 2721\n", - "Grvl 120\n", - "Pave 78\n", - "Name: Alley, dtype: int64" - ] - }, - "metadata": {}, - "execution_count": 13 - } - ] - }, - { - "cell_type": "code", - "source": [ - "##COLUNAS MARCILIO\n", - "\n", - "df[['Id', 'MSSubClass', 'MSZoning', 'LotFrontage', 'LotArea', 'Street',\n", - " 'Alley', 'LotShape', 'LandContour', 'Utilities', 'LotConfig',\n", - " 'LandSlope', 'Neighborhood', 'Condition1', 'Condition2', 'BldgType',\n", - " 'HouseStyle', 'OverallQual', 'OverallCond', 'YearBuilt', 'YearRemodAdd',\n", - " 'RoofStyle', 'RoofMatl', 'Exterior1st', 'Exterior2nd', 'MasVnrType',\n", - " 'MasVnrArea', 'ExterQual']].describe().round(2)" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 0 - }, - "id": "yOngNXx5L8i5", - "outputId": "afe08f46-5720-43fe-e23b-af8fd3074b28" - }, - "execution_count": 14, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - " Id MSSubClass LotFrontage LotArea OverallQual OverallCond \\\n", - "count 2919.00 2919.00 2433.00 2919.00 2919.00 2919.00 \n", - "mean 1460.00 57.14 69.31 10168.11 6.09 5.56 \n", - "std 842.79 42.52 23.34 7887.00 1.41 1.11 \n", - "min 1.00 20.00 21.00 1300.00 1.00 1.00 \n", - "25% 730.50 20.00 59.00 7478.00 5.00 5.00 \n", - "50% 1460.00 50.00 68.00 9453.00 6.00 5.00 \n", - "75% 2189.50 70.00 80.00 11570.00 7.00 6.00 \n", - "max 2919.00 190.00 313.00 215245.00 10.00 9.00 \n", - "\n", - " YearBuilt YearRemodAdd MasVnrArea \n", - "count 2919.00 2919.00 2896.00 \n", - "mean 1971.31 1984.26 102.20 \n", - "std 30.29 20.89 179.33 \n", - "min 1872.00 1950.00 0.00 \n", - "25% 1953.50 1965.00 0.00 \n", - "50% 1973.00 1993.00 0.00 \n", - "75% 2001.00 2004.00 164.00 \n", - "max 2010.00 2010.00 1600.00 " - ], - "text/html": [ - "\n", - "
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mean1460.0057.1469.3110168.116.095.561971.311984.26102.20
std842.7942.5223.347887.001.411.1130.2920.89179.33
min1.0020.0021.001300.001.001.001872.001950.000.00
25%730.5020.0059.007478.005.005.001953.501965.000.00
50%1460.0050.0068.009453.006.005.001973.001993.000.00
75%2189.5070.0080.0011570.007.006.002001.002004.00164.00
max2919.00190.00313.00215245.0010.009.002010.002010.001600.00
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\n", - " " - ] - }, - "metadata": {}, - "execution_count": 14 - } - ] - }, - { - "cell_type": "code", - "source": [ - "## Nenhuma delas tem constante porque o desvio padrão não foi igual a 0 em nenhum caso." - ], - "metadata": { - "id": "mmW-cFqHvZkt" - }, - "execution_count": 15, - "outputs": [] - }, - { - "cell_type": "markdown", - "source": [ - "####**Outro exemplo de tratamento que poderia ser aplicado**" - ], - "metadata": { - "id": "LFrdi2q1pEXW" - } - }, - { - "cell_type": "markdown", - "source": [ - "Como nosso objetivo principal neste exercício não é treinar a habilidade de tratar os dados (porque já fizemos isso em outros exercícios), fomos orientados a não perder muito tempo fazendo todos os tratamentos possíveis. \n", - "\n", - "Porém, abaixo há um modelo de tratamento que poderia ser adotado para todas as variáveis categóricas." - ], - "metadata": { - "id": "dKHUTe58E5dO" - } - }, - { - "cell_type": "code", - "source": [ - "ls_sub= {'LotShape': {'Reg':4, 'IR1':3, 'IR2':2, 'IR3':1}}\n", - "df_2=df.copy()\n", - "df_2=df.replace(ls_sub)\n", - "df_2[['LotShape']]" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 0 - }, - "id": "vGeWyPG-6y3i", - "outputId": "da8cf0a3-136b-49d7-a212-686b2c30a297" - }, - "execution_count": 16, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - " LotShape\n", - "0 4\n", - "1 3\n", - "2 3\n", - "3 3\n", - "4 3\n", - "... ...\n", - "1455 4\n", - "1456 4\n", - "1457 4\n", - "1458 4\n", - "1459 4\n", - "\n", - "[2919 rows x 1 columns]" - ], - "text/html": [ - "\n", - "
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\n", - " " - ] - }, - "metadata": {}, - "execution_count": 16 - } - ] - }, - { - "cell_type": "markdown", - "source": [ - "### ***Análise de Correlação para Regressão***" - ], - "metadata": { - "id": "XOESx5YXICzj" - } - }, - { - "cell_type": "markdown", - "source": [ - "***Análise da correlação entre as variáveis que eu fiquei responsável e a variável dependente (saleprice)***" - ], - "metadata": { - "id": "bswLkMHIfjDa" - } - }, - { - "cell_type": "code", - "source": [ - "cm1=df[['MSSubClass', 'MSZoning', 'LotFrontage', 'LotArea', 'Street',\n", - " 'Alley', 'LotShape', 'LandContour', 'Utilities', 'LotConfig',\n", - " 'LandSlope', 'Neighborhood', 'Condition1', 'Condition2', 'BldgType',\n", - " 'HouseStyle', 'OverallQual', 'OverallCond', 'YearBuilt', 'YearRemodAdd',\n", - " 'RoofStyle', 'RoofMatl', 'Exterior1st', 'Exterior2nd', 'MasVnrType',\n", - " 'MasVnrArea', 'ExterQual', 'SalePrice']].corr().round(2)" - ], - "metadata": { - "id": "QBai1I1-46kL" - }, - "execution_count": 17, - "outputs": [] - }, - { - "cell_type": "code", - "source": [ - "fig, ax = plt.subplots(figsize=(24,16))\n", - "sns.heatmap(cm1, ax=ax, vmin=-1.0,vmax=1.0, annot=True,cmap='RdYlGn') \n", - "plt.show()" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 0 - }, - "id": "RPOUg5rZ_UZM", - "outputId": "338b3db8-3814-4557-96d4-be066f255888" - }, - "execution_count": 18, - "outputs": [ - { - "output_type": "display_data", - "data": { - "text/plain": [ - "
" - ], - "image/png": 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\n" - }, - "metadata": { - "needs_background": "light" - } - } - ] - }, - { - "cell_type": "markdown", - "source": [ - "**Análise da correlação unindo as minhas com as variáveis dos colegas**" - ], - "metadata": { - "id": "udPMaPm6rSny" - } - }, - { - "cell_type": "code", - "source": [ - "cm2=df[[ 'SalePrice','MasVnrArea', 'LotFrontage', 'GarageYrBlt','OverallQual', 'YearBuilt', 'YearRemodAdd', 'Fireplaces', \n", - " 'GarageCars', 'GarageArea', 'BsmtFinSF1', 'TotalBsmtSF',\n", - " '1stFlrSF','GrLivArea', 'FullBath']].corr().round(2)" - ], - "metadata": { - "id": "_ko52gkuMkjB" - }, - "execution_count": 19, - "outputs": [] - }, - { - "cell_type": "code", - "source": [ - "fig, ax = plt.subplots(figsize=(24,16))\n", - "sns.heatmap(cm2, ax=ax, vmin=-1.0,vmax=1.0, annot=True,cmap='RdYlGn') \n", - "plt.show()" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 0 - }, - "id": "7IrogIZ3NFH0", - "outputId": "df58f3a4-eaf9-4db2-cf83-3675bbe8f59f" - }, - "execution_count": 20, - "outputs": [ - { - "output_type": "display_data", - "data": { - "text/plain": [ - "
" - ], - "image/png": 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- }, - "metadata": { - "needs_background": "light" - } - } - ] - }, - { - "cell_type": "markdown", - "source": [ - "### **Estimando o 'SalePrice' com base nas variáveis definidas pela análise de correlação**" - ], - "metadata": { - "id": "96l1c5czsVbE" - } - }, - { - "cell_type": "markdown", - "source": [ - "####**Aplicando a regressão na tabela de treino**" - ], - "metadata": { - "id": "z_xoJmY7-nM7" - } - }, - { - "cell_type": "markdown", - "source": [ - "Primeiro, vamos separar os dados de novo" - ], - "metadata": { - "id": "5hewDznG40ue" - } - }, - { - "cell_type": "code", - "source": [ - "df_test_2=df[df['istrain']==0]" - ], - "metadata": { - "id": "opGvH4cHvh7L" - }, - "execution_count": 21, - "outputs": [] - }, - { - "cell_type": "code", - "source": [ - "df_train_2=df[df['istrain']==1]" - ], - "metadata": { - "id": "ozjhtinEvzJb" - }, - "execution_count": 22, - "outputs": [] - }, - { - "cell_type": "code", - "source": [ - "df_train_2.head()" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 0 - }, - "id": "g55RGTAFvvt1", - "outputId": "4b13ee8d-323c-439f-bd9c-e5b701c37af9" - }, - "execution_count": 23, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - " Id MSSubClass MSZoning LotFrontage LotArea Street Alley LotShape \\\n", - "0 1 60 RL 65.0 8450 Pave no_access Reg \n", - "1 2 20 RL 80.0 9600 Pave no_access Reg \n", - "2 3 60 RL 68.0 11250 Pave no_access IR1 \n", - "3 4 70 RL 60.0 9550 Pave no_access IR1 \n", - "4 5 60 RL 84.0 14260 Pave no_access IR1 \n", - "\n", - " LandContour Utilities ... PoolQC Fence MiscFeature MiscVal MoSold \\\n", - "0 Lvl AllPub ... no_pool no_fence none 0 2 \n", - "1 Lvl AllPub ... no_pool no_fence none 0 5 \n", - "2 Lvl AllPub ... no_pool no_fence none 0 9 \n", - "3 Lvl AllPub ... no_pool no_fence none 0 2 \n", - "4 Lvl AllPub ... no_pool no_fence none 0 12 \n", - "\n", - " YrSold SaleType SaleCondition istrain SalePrice \n", - "0 2008 WD Normal 1 208500.0 \n", - "1 2007 WD Normal 1 181500.0 \n", - "2 2008 WD Normal 1 223500.0 \n", - "3 2006 WD Abnorml 1 140000.0 \n", - "4 2008 WD Normal 1 250000.0 \n", - "\n", - "[5 rows x 82 columns]" - ], - "text/html": [ - "\n", - "
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IdMSSubClassMSZoningLotFrontageLotAreaStreetAlleyLotShapeLandContourUtilities...PoolQCFenceMiscFeatureMiscValMoSoldYrSoldSaleTypeSaleConditionistrainSalePrice
0160RL65.08450Paveno_accessRegLvlAllPub...no_poolno_fencenone022008WDNormal1208500.0
1220RL80.09600Paveno_accessRegLvlAllPub...no_poolno_fencenone052007WDNormal1181500.0
2360RL68.011250Paveno_accessIR1LvlAllPub...no_poolno_fencenone092008WDNormal1223500.0
3470RL60.09550Paveno_accessIR1LvlAllPub...no_poolno_fencenone022006WDAbnorml1140000.0
4560RL84.014260Paveno_accessIR1LvlAllPub...no_poolno_fencenone0122008WDNormal1250000.0
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\n", - " " - ] - }, - "metadata": {}, - "execution_count": 23 - } - ] - }, - { - "cell_type": "markdown", - "source": [ - "As variáveis explicativas escolhidas fazem parte do grupo de variáveis da matriz de correlação 2. Porém, a fim de simplificar o processo, optei por retirar variáveis com valores nulos." - ], - "metadata": { - "id": "fD52O25i1Vdd" - } - }, - { - "cell_type": "code", - "source": [ - "lr=LinearRegression()\n", - "X=df_train_2[['OverallQual', 'YearBuilt', 'YearRemodAdd', 'Fireplaces', \n", - " 'GarageCars', 'GarageArea', 'BsmtFinSF1', 'TotalBsmtSF',\n", - " '1stFlrSF','GrLivArea', 'FullBath']]\n", - "Y=df_train_2[['SalePrice']]" - ], - "metadata": { - "id": "7CsuVLQQwxPn" - }, - "execution_count": 24, - "outputs": [] - }, - { - "cell_type": "markdown", - "source": [ - "Vou separar para o teste 30% da amostra e para validação 70%, esse método é chamado de holdout e é bem aceito no meio corporativo. 70% da amostra irá passar pelo FIT e os outros 30% serão utilizados para teste. O random state irá fixar ou não um valor para o train, teste e split começar. " - ], - "metadata": { - "id": "J4tRtLZR1IHl" - } - }, - { - "cell_type": "code", - "source": [ - "X_train, X_valid, Y_train, Y_valid = train_test_split(X, Y, random_state=42, test_size=0.3) " - ], - "metadata": { - "id": "omS1QI5EsUCA" - }, - "execution_count": 25, - "outputs": [] - }, - { - "cell_type": "markdown", - "source": [ - "Fitando o modelo de regressão linear com as variáveis explicativas e dependente de treino." - ], - "metadata": { - "id": "5ZiDAj6wZ-7C" - } - }, - { - "cell_type": "code", - "source": [ - "lr.fit(X_train,Y_train)" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "CVl4KDZR1ENk", - "outputId": "b0c5841c-4d93-432f-bfa8-cee21df7795b" - }, - "execution_count": 26, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "LinearRegression()" - ] - }, - "metadata": {}, - "execution_count": 26 - } - ] - }, - { - "cell_type": "markdown", - "source": [ - "Criando uma matriz de estimativas do SalePrice com base na matriz das variáveis explicativas de validação." - 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\n", - " " - ] - }, - "metadata": {}, - "execution_count": 29 - } - ] - }, - { - "cell_type": "markdown", - "source": [ - "Importando as métricas e verificando a explicabilidade do meu modelo." - ], - "metadata": { - "id": "0wYP9glCahj3" - } - }, - { - "cell_type": "code", - "source": [ - "r2= r2_score(Y_valid,Yhat)\n", - "print('As variáveis explicativas do modelo explicam as variações no preço de venda dos imóveis em:',(r2*100).round(2),'%')" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "9RKNUnC_3ky3", - "outputId": "dab95c1e-e22c-4d5f-d495-70fb9ba743fa" - }, - "execution_count": 30, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "As variáveis explicativas do modelo explicam as variações no preço de venda dos imóveis em: 80.83 %\n" - ] - } - ] - }, - { - "cell_type": "code", - "source": [ - "m_abe=mean_absolute_error(Y_valid,Yhat)\n", - "print('O erro médio absoluto do modelo é:', (m_abe).round(2))" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "bM8USBis32KO", - "outputId": "5b6add35-3199-45e5-97a5-ed026cb83b48" - }, - "execution_count": 31, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "O erro médio absoluto do modelo é: 24232.33\n" - ] - } - ] - }, - { - "cell_type": "code", - "source": [ - "m_sqe=mean_squared_error(Y_valid,Yhat)\n", - "print('O erro médio quadrático do modelo é:', (m_sqe).round(2))" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "rgGd14DT4Yja", - "outputId": "5743e120-38d3-48c1-87af-214d8097ba75" - }, - "execution_count": 32, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "O erro médio quadrático do modelo é: 1337499031.46\n" - ] - } - ] - }, - { - "cell_type": "code", - "source": [ - "m_sqe_sqrt=math.sqrt(m_sqe)\n", - "print('A raiz quadrada do erro médio quadrático é:', (m_sqe_sqrt))" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "T5a0AjLLhj6M", - "outputId": "e23a9eed-4511-4dcb-a65b-962c18ca0de1" - }, - "execution_count": 33, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "A raiz quadrada do erro médio quadrático é: 36571.833854163844\n" - ] - } - ] - }, - { - "cell_type": "markdown", - "source": [ - "1. Modelo A:\n", - "\n", - " *R2: 80,83%*\n", - "\n", - " *MAE: 24.232,33 UM*\n", - "\n", - " *MSE: 1.337.499.031,46 UM*" - ], - "metadata": { - "id": "PeBepZAubLI6" - } - }, - { - "cell_type": "code", - "source": [ - "lr.coef_" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "EwjKTRJgdZQg", - "outputId": "7c4b6482-7df7-46ba-a46a-f4fc3517579d" - }, - "execution_count": 34, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "array([[ 1.92568904e+04, 2.27841743e+02, 3.47997538e+02,\n", - " 9.61594675e+03, 1.50860924e+04, 5.37442854e+00,\n", - " 1.87733318e+01, 3.55529028e+00, 1.05877616e+01,\n", - " 4.13827981e+01, -1.58648955e+03]])" - ] - }, - "metadata": {}, - "execution_count": 34 - } - ] - }, - { - "cell_type": "code", - "source": [ - "lr.intercept_" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "QQ3OJWAwdk2V", - "outputId": "16df4e65-5477-4fd8-b77f-8e329840bda4" - }, - "execution_count": 35, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "array([-1197139.32509137])" - ] - }, - "metadata": {}, - "execution_count": 35 - } - ] - }, - { - "cell_type": "markdown", - "source": [ - "Salvando os resultados do melhor modelo com a biblioteca pickle" - ], - "metadata": { - "id": "dq_kYsxeekuO" - } - }, - { - "cell_type": "code", - "source": [ - "with open('LinearRegression.pkl', 'wb') as modelo:\n", - " pickle.dump(lr,modelo) " - ], - "metadata": { - "id": "1g0Bq1steai-" - }, - "execution_count": 36, - "outputs": [] - }, - { - "cell_type": "code", - "source": [ - "with open('LinearRegression.pkl', 'rb') as modelo:\n", - " regressao=pickle.load(modelo)" - ], - "metadata": { - "id": "hYxn6xcEecqf" - }, - "execution_count": 37, - "outputs": [] - }, - { - "cell_type": "code", - "source": [ - "regressao" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "vPeLorKgelZ7", - "outputId": "909b3ecb-0220-4b6c-ccb1-6f14b6898566" - }, - "execution_count": 38, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "LinearRegression()" - ] - }, - "metadata": {}, - "execution_count": 38 - } - ] - }, - { - "cell_type": "markdown", - "source": [ - "####**Aplicando a regressão na tabela de teste**" - ], - "metadata": { - "id": "BmMG5poglBDK" - } - }, - { - "cell_type": "markdown", - "source": [ - "Antes de mais nada, vamos verificar se há nulos nas variáveis explicativas para evitar problemas na hora de estimar a regressão" - ], - "metadata": { - "id": "ad9qI2tiqewk" - } - }, - { - "cell_type": "code", - "source": [ - "df_test_2[['OverallQual', 'YearBuilt', 'YearRemodAdd', 'Fireplaces', \n", - " 'GarageCars', 'GarageArea', 'BsmtFinSF1', 'TotalBsmtSF',\n", - " '1stFlrSF','GrLivArea', 'FullBath']].info()" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "2tE6KD7omY8P", - "outputId": "89459642-bc8b-4ada-c1ab-69e088fba9a5" - }, - "execution_count": 39, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "\n", - "Int64Index: 1459 entries, 0 to 1458\n", - "Data columns (total 11 columns):\n", - " # Column Non-Null Count Dtype \n", - "--- ------ -------------- ----- \n", - " 0 OverallQual 1459 non-null int64 \n", - " 1 YearBuilt 1459 non-null int64 \n", - " 2 YearRemodAdd 1459 non-null int64 \n", - " 3 Fireplaces 1459 non-null int64 \n", - " 4 GarageCars 1458 non-null float64\n", - " 5 GarageArea 1458 non-null float64\n", - " 6 BsmtFinSF1 1458 non-null float64\n", - " 7 TotalBsmtSF 1458 non-null float64\n", - " 8 1stFlrSF 1459 non-null int64 \n", - " 9 GrLivArea 1459 non-null int64 \n", - " 10 FullBath 1459 non-null int64 \n", - "dtypes: float64(4), int64(7)\n", - "memory usage: 136.8 KB\n" - ] - } - ] - }, - { - "cell_type": "markdown", - "source": [ - "As variáveis: \n", - "\n", - "1. 'GarageCars'\n", - "\n", - "2. 'GarageArea'\n", - "\n", - "3. 'BsmtFinSF1'\n", - "\n", - "4. 'TotalBsmtSF'\n", - "\n", - "Apresentam 1 observação com dados nulos.\n", - "\n", - "Vamos substituir esses nulos por" - ], - "metadata": { - "id": "JWHeo_Icqtqx" - } - }, - { - "cell_type": "code", - "source": [ - "df_test_2[['GarageCars','GarageArea', 'BsmtFinSF1','TotalBsmtSF']].mean().round(2)" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "vw32OUMcpjU3", - "outputId": "56fbb400-c84d-4331-ceea-23301d8222d8" - }, - "execution_count": 40, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "GarageCars 1.77\n", - "GarageArea 472.77\n", - "BsmtFinSF1 439.20\n", - "TotalBsmtSF 1046.12\n", - "dtype: float64" - ] - }, - "metadata": {}, - "execution_count": 40 - } - ] - }, - { - "cell_type": "code", - "source": [ - "df_test_2[['GarageCars','GarageArea', 'BsmtFinSF1','TotalBsmtSF']].median().round(2)" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "f-OQ_5njp9um", - "outputId": "797afee7-3d4e-4413-d1b6-0fd83c76c7d1" - }, - "execution_count": 41, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "GarageCars 2.0\n", - "GarageArea 480.0\n", - "BsmtFinSF1 350.5\n", - "TotalBsmtSF 988.0\n", - "dtype: float64" - ] - }, - "metadata": {}, - "execution_count": 41 - } - ] - }, - { - "cell_type": "markdown", - "source": [ - "Para me ajudar a decidir se substituo pela média ou mediana, vou plotar boxplots." - ], - "metadata": { - "id": "_dgrzsqsxsBI" - } - }, - { - "cell_type": "code", - "source": [ - "fig, axes =plt.subplots(2,2,figsize=[10,10])\n", - "\n", - "sns.boxplot(data=df_test_2,x='GarageCars',ax=axes[0,0],palette='pastel').set_title('Nº de Carros na Garagem')\n", - "sns.boxplot(data=df_test_2, x='GarageArea',ax=axes[0,1],palette='dark').set_title('Área da Garagem')\n", - "sns.boxplot(data=df_test_2,x='BsmtFinSF1',ax=axes[1,0],palette='bright').set_title('Ft² de Porão Tipo 1 Finalizado')\n", - "sns.boxplot(data=df_test_2,x='TotalBsmtSF',ax=axes[1,1],palette='deep').set_title('Ft² total do porão')\n", - "\n", - "fig.suptitle('BoxPlots',fontsize=20)\n", - "\n", - "sns.boxplot(data=df_test_2,x=df_test_2['TotalBsmtSF'])\n", - "plt.show()" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 0 - }, - "id": "JmfgcrWyrxXg", - "outputId": "dcfabab8-0178-472e-939d-f8ab8462bc8d" - }, - "execution_count": 42, - "outputs": [ - { - "output_type": "display_data", - "data": { - "text/plain": [ - "
" - ], - "image/png": 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\n" - }, - "metadata": { - "needs_background": "light" - } - } - ] - }, - { - "cell_type": "markdown", - "source": [ - "Ok, agora vamos fazer a substituição utilizando a mediana para diminuir o impacto de outliers nos nossos resultados" - ], - "metadata": { - "id": "93ZeVHUNxzkb" - } - }, - { - "cell_type": "code", - "source": [ - "df_test_2['GarageCars'].fillna(df_test_2['GarageCars'].median(),inplace=True)\n", - "df_test_2['GarageArea'].fillna(df_test_2['GarageArea'].median(),inplace=True)\n", - "df_test_2['BsmtFinSF1'].fillna(df_test_2['BsmtFinSF1'].median(),inplace=True)\n", - "df_test_2['TotalBsmtSF'].fillna(df_test_2['TotalBsmtSF'].median(),inplace=True)" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "8Db7R0zCnDGk", - "outputId": "b98a0912-a5a4-4ed2-b017-1bf6b786e0fb" - }, - "execution_count": 43, - "outputs": [ - { - "output_type": "stream", - "name": "stderr", - "text": [ - "/usr/local/lib/python3.7/dist-packages/pandas/core/generic.py:6392: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " return self._update_inplace(result)\n", - "/usr/local/lib/python3.7/dist-packages/pandas/core/generic.py:6392: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " return self._update_inplace(result)\n", - "/usr/local/lib/python3.7/dist-packages/pandas/core/generic.py:6392: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " return self._update_inplace(result)\n", - "/usr/local/lib/python3.7/dist-packages/pandas/core/generic.py:6392: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " return self._update_inplace(result)\n" - ] - } - ] - }, - { - "cell_type": "markdown", - "source": [ - "Agora vamos verificar se deu certo, e caso funcione vamos estimar o Sale Price para o modelo com as variáveis explicativas da base de teste." - ], - "metadata": { - "id": "p7gRm1MA4mXA" - } - }, - { - "cell_type": "code", - "source": [ - "df_test_2[['OverallQual', 'YearBuilt', 'YearRemodAdd', 'Fireplaces', \n", - " 'GarageCars', 'GarageArea', 'BsmtFinSF1', 'TotalBsmtSF',\n", - " '1stFlrSF','GrLivArea', 'FullBath']].info()" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "RnBuW91fye7B", - "outputId": "55f22f58-c62c-4b2f-bb58-8e2d19a86d32" - }, - "execution_count": 44, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "\n", - "Int64Index: 1459 entries, 0 to 1458\n", - "Data columns (total 11 columns):\n", - " # Column Non-Null Count Dtype \n", - "--- ------ -------------- ----- \n", - " 0 OverallQual 1459 non-null int64 \n", - " 1 YearBuilt 1459 non-null int64 \n", - " 2 YearRemodAdd 1459 non-null int64 \n", - " 3 Fireplaces 1459 non-null int64 \n", - " 4 GarageCars 1459 non-null float64\n", - " 5 GarageArea 1459 non-null float64\n", - " 6 BsmtFinSF1 1459 non-null float64\n", - " 7 TotalBsmtSF 1459 non-null float64\n", - " 8 1stFlrSF 1459 non-null int64 \n", - " 9 GrLivArea 1459 non-null int64 \n", - " 10 FullBath 1459 non-null int64 \n", - "dtypes: float64(4), int64(7)\n", - "memory usage: 136.8 KB\n" - ] - } - ] - }, - { - "cell_type": "code", - "source": [ - "X_2=df_test_2[['OverallQual', 'YearBuilt', 'YearRemodAdd', 'Fireplaces', \n", - " 'GarageCars', 'GarageArea', 'BsmtFinSF1', 'TotalBsmtSF',\n", - " '1stFlrSF','GrLivArea', 'FullBath']]" - ], - "metadata": { - "id": "egiGP2zefq52" - }, - "execution_count": 45, - "outputs": [] - }, - { - "cell_type": "code", - "source": [ - "Yhat_2=regressao.predict(X_2).round(2)\n", - "Yhat_2" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "bl_VsVBOlnni", - "outputId": "81659529-0407-4098-ea78-4f464e84e60c" - }, - "execution_count": 46, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "array([[104276.2 ],\n", - " [152193.39],\n", - " [184036.83],\n", - " ...,\n", - " [172557.54],\n", - " [104610.97],\n", - " [251931.48]])" - ] - }, - "metadata": {}, - "execution_count": 46 - } - ] - }, - { - "cell_type": "markdown", - "source": [ - "Preços estimados, agora vamos incluí-los em uma nova variável na tabela e substituir os nulos da variável antiga pelos valores da variável nova." - ], - "metadata": { - "id": "o3POmRWh7rUl" - } - }, - { - "cell_type": "code", - "source": [ - "df_test_2[['SalePrice_est']]=Yhat_2\n", - "df_test_2.head()" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 0 - }, - "id": "FMP0ep5N6H3P", - "outputId": "2f3a143b-8f86-4b83-d393-7613b323a8ae" - }, - "execution_count": 47, - "outputs": [ - { - "output_type": "stream", - "name": "stderr", - "text": [ - "/usr/local/lib/python3.7/dist-packages/pandas/core/frame.py:3678: SettingWithCopyWarning: \n", - "A value is trying to be set on a copy of a slice from a DataFrame.\n", - "Try using .loc[row_indexer,col_indexer] = value instead\n", - "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " self[col] = igetitem(value, i)\n" - ] - }, - { - "output_type": "execute_result", - "data": { - "text/plain": [ - " Id MSSubClass MSZoning LotFrontage LotArea Street Alley 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Presently they have around several hundred thousands of registered customers and serve almost one million consumers a year. They sell products from 5 major categories: wines, rare meat products, exotic fruits, specially prepared fish and sweet products. These can further be divided into gold and regular products. The customers can order and acquire products through 3 sales channels: physical stores, catalogs and the company’s website. Globally, the company had solid revenues and a healthy bottom line in the past 3 years, but the profit growth perspectives for the next 3 years are not promising... For this reason, several strategic initiatives are being considered to invert this situation. One is to improve the performance of marketing activities, with a special focus on marketing campaigns.\n", - "\n", - "**The Marketing Department**\n", - "\n", - "The marketing department was pressured to spend its annual budget more wisely. The CMO perceives the importance of having a more quantitative approach when taking decisions, reason why a small team of data scientists was hired with a clear objective in mind: to build a solution which will support direct marketing initiatives. Desirably, the success of these activities will prove the value of the approach and convince the more skeptical within the company\n", - "\n", - "**The Objective**\n", - "\n", - "The objective of the team is to build an analysis to address the highest profit for the next direct marketing campaign, scheduled for the next month. The new campaign, sixth, aims at selling a new gadget to the Customer Database. To build the analysis, a pilot campaign involving 2.240 customers was carried out. The customers were selected at random and contacted by phone regarding the acquisition of the gadget. During the following months, customers who bought the\n", - "offer were properly labeled. The total cost of the sample campaign was 6.720MU and the revenue generated by the customers who accepted the offer was 3.674MU. Globally the campaign had a profit of -3.046MU. The success rate of the campaign was 15%.\n", - "\n" - ], - "metadata": { - "id": "PemOd0DeT9hb" - } - }, - { - "cell_type": "markdown", - "source": [ - "##**SOLUÇÃO:**" - ], - "metadata": { - "id": "hGqXx8oVYPeT" - } - }, - { - "cell_type": "markdown", - "source": [ - "###***Importanto os dados e algumas bibliotecas***" - ], - "metadata": { - "id": "XuHJ7E9C0uJF" - } - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "id": "BpFBNbrtR8Gz" - }, - "outputs": [], - "source": [ - "## Importando as bibliotecas que eu vou usar\n", - "\n", - "import numpy as np\n", - "import pandas as pd\n", - "import matplotlib.pyplot as plt\n", - "import seaborn as sns" - ] - }, - { - "cell_type": "code", - "source": [ - "## Importando a base e criando um dataframe df\n", - "\n", - "df=pd.read_csv('https://raw.githubusercontent.com/ifood/ifood-data-analyst-case/main/retail_case_data.csv')" - ], - "metadata": { - "id": "V77pOoVRS1PI" - }, - "execution_count": 2, - "outputs": [] - }, - { - "cell_type": "code", - "source": [ - "## Vendo se deu tudo certo\n", - "\n", - "df" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 0 - }, - "id": "l6wJLnQ9ZMBU", - "outputId": "2a1a6b24-fa4d-40d2-f5d9-afe18fa12b1e" - }, - "execution_count": 3, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - " ID Year_Birth Education Marital_Status Income Kidhome \\\n", - "0 5524 1957 Graduation Single 58138.0 0 \n", - "1 2174 1954 Graduation Single 46344.0 1 \n", - "2 4141 1965 Graduation Together 71613.0 0 \n", - "3 6182 1984 Graduation Together 26646.0 1 \n", - "4 5324 1981 PhD Married 58293.0 1 \n", - "... ... ... ... ... ... ... \n", - "2235 10870 1967 Graduation Married 61223.0 0 \n", - "2236 4001 1946 PhD Together 64014.0 2 \n", - "2237 7270 1981 Graduation Divorced 56981.0 0 \n", - "2238 8235 1956 Master Together 69245.0 0 \n", - "2239 9405 1954 PhD Married 52869.0 1 \n", - "\n", - " Teenhome Dt_Customer Recency MntWines ... NumWebVisitsMonth \\\n", - "0 0 2012-09-04 58 635 ... 7 \n", - "1 1 2014-03-08 38 11 ... 5 \n", - "2 0 2013-08-21 26 426 ... 4 \n", - "3 0 2014-02-10 26 11 ... 6 \n", - "4 0 2014-01-19 94 173 ... 5 \n", - "... ... ... ... ... ... ... \n", - "2235 1 2013-06-13 46 709 ... 5 \n", - "2236 1 2014-06-10 56 406 ... 7 \n", - "2237 0 2014-01-25 91 908 ... 6 \n", - "2238 1 2014-01-24 8 428 ... 3 \n", - "2239 1 2012-10-15 40 84 ... 7 \n", - "\n", - " AcceptedCmp3 AcceptedCmp4 AcceptedCmp5 AcceptedCmp1 AcceptedCmp2 \\\n", - "0 0 0 0 0 0 \n", - "1 0 0 0 0 0 \n", - "2 0 0 0 0 0 \n", - "3 0 0 0 0 0 \n", - "4 0 0 0 0 0 \n", - "... ... ... ... ... ... \n", - "2235 0 0 0 0 0 \n", - "2236 0 0 0 1 0 \n", - "2237 0 1 0 0 0 \n", - "2238 0 0 0 0 0 \n", - "2239 0 0 0 0 0 \n", - "\n", - " Complain Z_CostContact Z_Revenue Response \n", - "0 0 3 11 1 \n", - "1 0 3 11 0 \n", - "2 0 3 11 0 \n", - "3 0 3 11 0 \n", - "4 0 3 11 0 \n", - "... ... ... ... ... \n", - "2235 0 3 11 0 \n", - "2236 0 3 11 0 \n", - "2237 0 3 11 0 \n", - "2238 0 3 11 0 \n", - "2239 0 3 11 1 \n", - "\n", - "[2240 rows x 29 columns]" - ], - "text/html": [ - "\n", - "
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453241981PhDMarried58293.0102014-01-1994173...50000003110
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2235108701967GraduationMarried61223.0012013-06-1346709...50000003110
223640011946PhDTogether64014.0212014-06-1056406...70001003110
223772701981GraduationDivorced56981.0002014-01-2591908...60100003110
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2240 rows × 29 columns

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\n", - " \n", - " \n", - " \n", - "\n", - " \n", - "
\n", - "
\n", - " " - ] - }, - "metadata": {}, - "execution_count": 3 - } - ] - }, - { - "cell_type": "code", - "source": [ - "## Ok, a visualização deu certo, temos 2240 linhas e 29 colunas. \n", - "## Agora vamos começar a análise exploratória e descritiva dos dados." - ], - "metadata": { - "id": "T1kp9TtHZa7M" - }, - "execution_count": 4, - "outputs": [] - }, - { - "cell_type": "markdown", - "source": [ - "###***Análise exploratória***" - ], - "metadata": { - "id": "SNCXUTJDaOaM" - } - }, - { - "cell_type": "code", - "source": [ - "## Vendo as colunas do data frame para identificar com quais dados estamos trabalhando\n", - "df.columns" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "8AU1dNwEaM1Q", - "outputId": "23769625-07bf-4ced-9682-f7bc988e5540" - }, - "execution_count": 5, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "Index(['ID', 'Year_Birth', 'Education', 'Marital_Status', 'Income', 'Kidhome',\n", - " 'Teenhome', 'Dt_Customer', 'Recency', 'MntWines', 'MntFruits',\n", - " 'MntMeatProducts', 'MntFishProducts', 'MntSweetProducts',\n", - " 'MntGoldProds', 'NumDealsPurchases', 'NumWebPurchases',\n", - " 'NumCatalogPurchases', 'NumStorePurchases', 'NumWebVisitsMonth',\n", - " 'AcceptedCmp3', 'AcceptedCmp4', 'AcceptedCmp5', 'AcceptedCmp1',\n", - " 'AcceptedCmp2', 'Complain', 'Z_CostContact', 'Z_Revenue', 'Response'],\n", - " dtype='object')" - ] - }, - "metadata": {}, - "execution_count": 5 - } - ] - }, - { - "cell_type": "code", - "source": [ - "## Ok, é exatamente o que está descrito no PDF do desafio. " - ], - "metadata": { - "id": "76R6k-NKZdmp" - }, - "execution_count": 6, - "outputs": [] - }, - { - "cell_type": "code", - "source": [ - "## Para entender melhor nossos dados, o comando abaixo nos mostra a quantidade de valores não nulos de cada coluna e qual o tipo de informação que está armazenada (texto, int e etc)\n", - "df.info()" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "nuzjDRkmJ3Er", - "outputId": "f6f2c40d-b5f2-40a6-ed7e-2c79ffc89121" - }, - "execution_count": 7, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "\n", - "RangeIndex: 2240 entries, 0 to 2239\n", - "Data columns (total 29 columns):\n", - " # Column Non-Null Count Dtype \n", - "--- ------ -------------- ----- \n", - " 0 ID 2240 non-null int64 \n", - " 1 Year_Birth 2240 non-null int64 \n", - " 2 Education 2240 non-null object \n", - " 3 Marital_Status 2240 non-null object \n", - " 4 Income 2216 non-null float64\n", - " 5 Kidhome 2240 non-null int64 \n", - " 6 Teenhome 2240 non-null int64 \n", - " 7 Dt_Customer 2240 non-null object \n", - " 8 Recency 2240 non-null int64 \n", - " 9 MntWines 2240 non-null int64 \n", - " 10 MntFruits 2240 non-null int64 \n", - " 11 MntMeatProducts 2240 non-null int64 \n", - " 12 MntFishProducts 2240 non-null int64 \n", - " 13 MntSweetProducts 2240 non-null int64 \n", - " 14 MntGoldProds 2240 non-null int64 \n", - " 15 NumDealsPurchases 2240 non-null int64 \n", - " 16 NumWebPurchases 2240 non-null int64 \n", - " 17 NumCatalogPurchases 2240 non-null int64 \n", - " 18 NumStorePurchases 2240 non-null int64 \n", - " 19 NumWebVisitsMonth 2240 non-null int64 \n", - " 20 AcceptedCmp3 2240 non-null int64 \n", - " 21 AcceptedCmp4 2240 non-null int64 \n", - " 22 AcceptedCmp5 2240 non-null int64 \n", - " 23 AcceptedCmp1 2240 non-null int64 \n", - " 24 AcceptedCmp2 2240 non-null int64 \n", - " 25 Complain 2240 non-null int64 \n", - " 26 Z_CostContact 2240 non-null int64 \n", - " 27 Z_Revenue 2240 non-null int64 \n", - " 28 Response 2240 non-null int64 \n", - "dtypes: float64(1), int64(25), object(3)\n", - "memory usage: 507.6+ KB\n" - ] - } - ] - }, - { - "cell_type": "code", - "source": [ - "## Confirmando a quantidade de informações nulas de cada coluna, talvez vamos precisar disso depois.\n", - "df.isnull().sum()" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "g75Wh_rJY9OE", - "outputId": "8e4c25c2-eb91-4031-a240-447d58450745" - }, - "execution_count": 8, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "ID 0\n", - "Year_Birth 0\n", - "Education 0\n", - "Marital_Status 0\n", - "Income 24\n", - "Kidhome 0\n", - "Teenhome 0\n", - "Dt_Customer 0\n", - "Recency 0\n", - "MntWines 0\n", - "MntFruits 0\n", - "MntMeatProducts 0\n", - "MntFishProducts 0\n", - "MntSweetProducts 0\n", - "MntGoldProds 0\n", - "NumDealsPurchases 0\n", - "NumWebPurchases 0\n", - "NumCatalogPurchases 0\n", - "NumStorePurchases 0\n", - "NumWebVisitsMonth 0\n", - "AcceptedCmp3 0\n", - "AcceptedCmp4 0\n", - "AcceptedCmp5 0\n", - "AcceptedCmp1 0\n", - "AcceptedCmp2 0\n", - "Complain 0\n", - "Z_CostContact 0\n", - "Z_Revenue 0\n", - "Response 0\n", - "dtype: int64" - ] - }, - "metadata": {}, - "execution_count": 8 - } - ] - }, - { - "cell_type": "code", - "source": [ - "## 24 nulos na coluna da renda, depois vamos aplicar um filtro para esses dados" - ], - "metadata": { - "id": "RS6xvXLGZMQe" - }, - "execution_count": 9, - "outputs": [] - }, - { - "cell_type": "code", - "source": [ - "## Resumo estatístico das variáveis que não são dummies, texto ou data\n", - "\n", - "df[['Year_Birth','Income','Kidhome','Teenhome','Recency','MntWines','MntFruits','MntMeatProducts','MntFishProducts','MntSweetProducts','MntGoldProds', 'NumDealsPurchases', 'NumWebPurchases',\n", - " 'NumCatalogPurchases', 'NumStorePurchases', 'NumWebVisitsMonth', 'Z_CostContact', 'Z_Revenue']].describe().round(2)" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 0 - }, - "id": "gc-XmGvaOuz_", - "outputId": "4fa81b9c-0a64-400a-925d-0ba1444241a0" - }, - "execution_count": 10, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - " Year_Birth Income Kidhome Teenhome Recency MntWines MntFruits \\\n", - "count 2240.00 2216.00 2240.00 2240.00 2240.00 2240.00 2240.00 \n", - "mean 1968.81 52247.25 0.44 0.51 49.11 303.94 26.30 \n", - "std 11.98 25173.08 0.54 0.54 28.96 336.60 39.77 \n", - "min 1893.00 1730.00 0.00 0.00 0.00 0.00 0.00 \n", - "25% 1959.00 35303.00 0.00 0.00 24.00 23.75 1.00 \n", - "50% 1970.00 51381.50 0.00 0.00 49.00 173.50 8.00 \n", - "75% 1977.00 68522.00 1.00 1.00 74.00 504.25 33.00 \n", - "max 1996.00 666666.00 2.00 2.00 99.00 1493.00 199.00 \n", - "\n", - " MntMeatProducts MntFishProducts MntSweetProducts MntGoldProds \\\n", - "count 2240.00 2240.00 2240.00 2240.00 \n", - "mean 166.95 37.53 27.06 44.02 \n", - "std 225.72 54.63 41.28 52.17 \n", - "min 0.00 0.00 0.00 0.00 \n", - "25% 16.00 3.00 1.00 9.00 \n", - "50% 67.00 12.00 8.00 24.00 \n", - "75% 232.00 50.00 33.00 56.00 \n", - "max 1725.00 259.00 263.00 362.00 \n", - "\n", - " NumDealsPurchases NumWebPurchases NumCatalogPurchases \\\n", - "count 2240.00 2240.00 2240.00 \n", - "mean 2.33 4.08 2.66 \n", - "std 1.93 2.78 2.92 \n", - "min 0.00 0.00 0.00 \n", - "25% 1.00 2.00 0.00 \n", - "50% 2.00 4.00 2.00 \n", - "75% 3.00 6.00 4.00 \n", - "max 15.00 27.00 28.00 \n", - "\n", - " NumStorePurchases NumWebVisitsMonth Z_CostContact Z_Revenue \n", - "count 2240.00 2240.00 2240.0 2240.0 \n", - "mean 5.79 5.32 3.0 11.0 \n", - "std 3.25 2.43 0.0 0.0 \n", - "min 0.00 0.00 3.0 11.0 \n", - "25% 3.00 3.00 3.0 11.0 \n", - "50% 5.00 6.00 3.0 11.0 \n", - "75% 8.00 7.00 3.0 11.0 \n", - "max 13.00 20.00 3.0 11.0 " - ], - "text/html": [ - "\n", - "
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Year_BirthIncomeKidhomeTeenhomeRecencyMntWinesMntFruitsMntMeatProductsMntFishProductsMntSweetProductsMntGoldProdsNumDealsPurchasesNumWebPurchasesNumCatalogPurchasesNumStorePurchasesNumWebVisitsMonthZ_CostContactZ_Revenue
count2240.002216.002240.002240.002240.002240.002240.002240.002240.002240.002240.002240.002240.002240.002240.002240.002240.02240.0
mean1968.8152247.250.440.5149.11303.9426.30166.9537.5327.0644.022.334.082.665.795.323.011.0
std11.9825173.080.540.5428.96336.6039.77225.7254.6341.2852.171.932.782.923.252.430.00.0
min1893.001730.000.000.000.000.000.000.000.000.000.000.000.000.000.000.003.011.0
25%1959.0035303.000.000.0024.0023.751.0016.003.001.009.001.002.000.003.003.003.011.0
50%1970.0051381.500.000.0049.00173.508.0067.0012.008.0024.002.004.002.005.006.003.011.0
75%1977.0068522.001.001.0074.00504.2533.00232.0050.0033.0056.003.006.004.008.007.003.011.0
max1996.00666666.002.002.0099.001493.00199.001725.00259.00263.00362.0015.0027.0028.0013.0020.003.011.0
\n", - "
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\n", - " " - ] - }, - "metadata": {}, - "execution_count": 10 - } - ] - }, - { - "cell_type": "code", - "source": [ - "## Observações extraídas até então:\n", - "### Coluna de ano de nascimento: algum cliente inseriu data de nascimento em 1893, mas isso não faz sentido, iremos verificar depois.\n", - "### Coluna de renda: o valor mínimo dessa coluna (1730) e o valor máximo (666666) também estão destoando bastante da média e da mediana da renda. Verificar quem são esses cliente depois.\n", - "### Número de compras com descontos: o máximo é bem maior do que a média e a mediana, 15 compras. Talvez essa informação esteja errada.\n", - "## As colunas de custo do contrato e de receita são valores constantes, então possivelmente podemos \"dropá-las\"" - ], - "metadata": { - "id": "L0b4uDcpZnhy" - }, - "execution_count": 11, - "outputs": [] - }, - { - "cell_type": "code", - "source": [ - "## Agora, vamos ver quais são as categorias existentes dentro das variáveis categóricas:\n", - "df['Marital_Status'].unique()" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "6WqjQ1nbWIsa", - "outputId": "827d900d-6c77-4a12-c5ac-1307f9964630" - }, - "execution_count": 12, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "array(['Single', 'Together', 'Married', 'Divorced', 'Widow', 'Alone',\n", - " 'Absurd', 'YOLO'], dtype=object)" - ] - }, - "metadata": {}, - "execution_count": 12 - } - ] - }, - { - "cell_type": "code", - "source": [ - "## As categorias \"YOLO\" e \"Absurd\" não condizem com a informação que queremos. Provavelmente foi alguma \"zoeira\" de um usuário, portanto podemos deletá-las. \n", - "## A categoria Alone também não faz muito sentido, talvez podemos incluir suas observações na single, divorced ou widow." - ], - "metadata": { - "id": "u7ojNgvCdlfw" - }, - "execution_count": 13, - "outputs": [] - }, - { - "cell_type": "code", - "source": [ - "df['Education'].unique()" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "YSd_gZReXH7c", - "outputId": "594a1b57-0eb7-4524-8d42-5304658cfd90" - }, - "execution_count": 14, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "array(['Graduation', 'PhD', 'Master', 'Basic', '2n Cycle'], dtype=object)" - ] - }, - "metadata": {}, - "execution_count": 14 - } - ] - }, - { - "cell_type": "code", - "source": [ - "## Considerando que esses níveis de graduação são estadunidenses, temos que:\n", - "## 1) \"2n Cycle\" é o mesmo que o nível de \"Master\". Logo, vamos realizar essa substituição.\n", - "## 2) O nível chamado de \"graduation\", nos EUA, não é o mesmo que o nível de graduação no Brasil. Ele faz referência a estudantes com pós-graduação, mestrado ou doutorado.\n", - "## Como neste caso não sabemos se os estudantes de graduation são apenas os estudantes de pós ou se neles também estão incluídos os demais tipos (mestrado e doutorado), vou mantê-los de forma separada.\n", - "\n", - "## referência: https://www.estudarfora.org.br/graduate-e-undergraduate-diferenca/#:~:text=Nos%20Estados%20Unidos%2C%20os%20undergraduate,um%20curso%20de%20n%C3%ADvel%20undergraduate." - ], - "metadata": { - "id": "0T7zgD3ieASV" - }, - "execution_count": 15, - "outputs": [] - }, - { - "cell_type": "code", - "source": [ - "## Agora, vamos analisar variável de \"Data de cliente\" para entender mais sobre a base e sobre o intervalo temporal.\n", - "df['Dt_Customer'].min()" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 0 - }, - "id": "k_vwXj2DXlPq", - "outputId": "965f342c-858d-4bfe-dbce-169017d2737e" - }, - "execution_count": 16, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "'2012-07-30'" - ], - "application/vnd.google.colaboratory.intrinsic+json": { - "type": "string" - } - }, - "metadata": {}, - "execution_count": 16 - } - ] - }, - { - "cell_type": "code", - "source": [ - "df['Dt_Customer'].max()" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 0 - }, - "id": "IJHZ9Ka6Yury", - "outputId": "249076de-8692-43e5-f126-072c64e1b814" - }, - "execution_count": 17, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "'2014-06-29'" - ], - "application/vnd.google.colaboratory.intrinsic+json": { - "type": "string" - } - }, - "metadata": {}, - "execution_count": 17 - } - ] - }, - { - "cell_type": "code", - "source": [ - "## As datas indicam que a base começou em 2012 e termina em 2014. Isso é importante para nossa análise tbm." - ], - "metadata": { - "id": "fH9NhMUPeFp6" - }, - "execution_count": 18, - "outputs": [] - }, - { - "cell_type": "code", - "source": [ - "## Comentário final da AE:\n", - "\n", - "### Dropar colunas com var. constantes.\n", - "### Tratar dados da coluna de ano de nascimento (min).\n", - "### Tratar dados da coluna de estado civil (yolo, absurd, alone).\n", - "## Tratar dado da coluna de educação (2n cycle=Master)\n", - "### Tratar dados da renda: valor máximo, analisar valores mínimos e possivelmente substituir nulos.\n", - "### Avaliar coluna de compras com descontos: o máximo é bem maior do que a média e a mediana, 15 compras.\n" - ], - "metadata": { - "id": "AovdXspHP7Yr" - }, - "execution_count": 19, - "outputs": [] - }, - { - "cell_type": "markdown", - "source": [ - "### **Tratando os dados de acordo com o que foi identificado na AE**" - ], - "metadata": { - "id": "4BbPxrLrKWtp" - } - }, - { - "cell_type": "markdown", - "source": [ - "#### **Tratamentos iniciais (drops e substituições, exceto da income)**" - ], - "metadata": { - "id": "KgOKww7jhGyR" - } - }, - { - "cell_type": "code", - "source": [ - "## primeiro, vamos apagar as colunas de variáveis constantes que não iremos utilizar (custo=3 e receita=11)" - ], - "metadata": { - "id": "vR1dS5DInrX3" - }, - "execution_count": 20, - "outputs": [] - }, - { - "cell_type": "code", - "source": [ - "df.drop(columns=['Z_CostContact','Z_Revenue'],inplace=True)" - ], - "metadata": { - "id": "7VtOolHqnCFB" - }, - "execution_count": 21, - "outputs": [] - }, - { - "cell_type": "code", - "source": [ - "df" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 0 - }, - "id": "PfcpDT5ToBcY", - "outputId": "cddb8d96-25da-436f-9a2e-a31bcfd8b939" - }, - "execution_count": 22, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - " ID Year_Birth Education Marital_Status Income Kidhome \\\n", - "0 5524 1957 Graduation Single 58138.0 0 \n", - "1 2174 1954 Graduation Single 46344.0 1 \n", - "2 4141 1965 Graduation Together 71613.0 0 \n", - "3 6182 1984 Graduation Together 26646.0 1 \n", - "4 5324 1981 PhD Married 58293.0 1 \n", - "... ... ... ... ... ... ... \n", - "2235 10870 1967 Graduation Married 61223.0 0 \n", - "2236 4001 1946 PhD Together 64014.0 2 \n", - "2237 7270 1981 Graduation Divorced 56981.0 0 \n", - "2238 8235 1956 Master Together 69245.0 0 \n", - "2239 9405 1954 PhD Married 52869.0 1 \n", - "\n", - " Teenhome Dt_Customer Recency MntWines ... NumCatalogPurchases \\\n", - "0 0 2012-09-04 58 635 ... 10 \n", - "1 1 2014-03-08 38 11 ... 1 \n", - "2 0 2013-08-21 26 426 ... 2 \n", - "3 0 2014-02-10 26 11 ... 0 \n", - "4 0 2014-01-19 94 173 ... 3 \n", - "... ... ... ... ... ... ... \n", - "2235 1 2013-06-13 46 709 ... 3 \n", - "2236 1 2014-06-10 56 406 ... 2 \n", - "2237 0 2014-01-25 91 908 ... 3 \n", - "2238 1 2014-01-24 8 428 ... 5 \n", - "2239 1 2012-10-15 40 84 ... 1 \n", - "\n", - " NumStorePurchases NumWebVisitsMonth AcceptedCmp3 AcceptedCmp4 \\\n", - "0 4 7 0 0 \n", - "1 2 5 0 0 \n", - "2 10 4 0 0 \n", - "3 4 6 0 0 \n", - "4 6 5 0 0 \n", - "... ... ... ... ... \n", - "2235 4 5 0 0 \n", - "2236 5 7 0 0 \n", - "2237 13 6 0 1 \n", - "2238 10 3 0 0 \n", - "2239 4 7 0 0 \n", - "\n", - " AcceptedCmp5 AcceptedCmp1 AcceptedCmp2 Complain Response \n", - "0 0 0 0 0 1 \n", - "1 0 0 0 0 0 \n", - "2 0 0 0 0 0 \n", - "3 0 0 0 0 0 \n", - "4 0 0 0 0 0 \n", - "... ... ... ... ... ... \n", - "2235 0 0 0 0 0 \n", - "2236 0 1 0 0 0 \n", - "2237 0 0 0 0 0 \n", - "2238 0 0 0 0 0 \n", - "2239 0 0 0 0 1 \n", - "\n", - "[2240 rows x 27 columns]" - ], - "text/html": [ - "\n", - "
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\n", - " " - ] - }, - "metadata": {}, - "execution_count": 31 - } - ] - }, - { - "cell_type": "code", - "source": [ - "## vamos substituir os anos de 1900, 1899 e 1893 por dados nulos e depois vamos substituí-los pela média de ano de nascimento porque não faz sentido termos pessoas com mais de 110 anos de idade na base.\n", - "df['Year_Birth'].replace([1893,1899,1900], np.nan, inplace=True)" - ], - "metadata": { - "id": "Ngqq4Bd_XXzt" - }, - "execution_count": 32, - "outputs": [] - }, - { - "cell_type": "code", - "source": [ - "## se tiver funcionado, o novo ano mínimo tem que ser 1940\n", - "df['Year_Birth'].min()" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "p3AqXpwTYv2v", - "outputId": "b76ebe4e-6dec-4185-b50b-9ffe29ba643f" - }, - "execution_count": 33, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "1940.0" - ] - }, - "metadata": {}, - "execution_count": 33 - } - ] - }, - { - "cell_type": "code", - "source": [ - "## substituindo pela média\n", - "df['Year_Birth'].replace(np.nan, df['Year_Birth'].mean().astype(int), inplace=True)" - ], - "metadata": { - "id": "p3RX2GDJZIOn" - }, - "execution_count": 34, - "outputs": [] - }, - { - "cell_type": "code", - "source": [ - "## vendo se deu certo\n", - "df[df['ID']==11004]" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 0 - }, - "id": "UBgLYOLrZWqi", - "outputId": "5e485722-1226-4845-edaa-53cc6de58fad" - }, - "execution_count": 35, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - " ID Year_Birth Education Marital_Status Income Kidhome Teenhome \\\n", - "239 11004 1968.0 2n Cycle Single 60182.0 0 1 \n", - "\n", - " Dt_Customer Recency MntWines ... NumCatalogPurchases \\\n", - "239 2014-05-17 23 8 ... 0 \n", - "\n", - " NumStorePurchases NumWebVisitsMonth AcceptedCmp3 AcceptedCmp4 \\\n", - "239 2 4 0 0 \n", - "\n", - " AcceptedCmp5 AcceptedCmp1 AcceptedCmp2 Complain Response \n", - "239 0 0 0 0 0 \n", - "\n", - "[1 rows x 27 columns]" - ], - "text/html": [ - "\n", - "
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IDYear_BirthEducationMarital_StatusIncomeKidhomeTeenhomeDt_CustomerRecencyMntWines...NumCatalogPurchasesNumStorePurchasesNumWebVisitsMonthAcceptedCmp3AcceptedCmp4AcceptedCmp5AcceptedCmp1AcceptedCmp2ComplainResponse
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IDYear_BirthEducationMarital_StatusIncomeKidhomeTeenhomeDt_CustomerRecencyMntWines...NumCatalogPurchasesNumStorePurchasesNumWebVisitsMonthAcceptedCmp3AcceptedCmp4AcceptedCmp5AcceptedCmp1AcceptedCmp2ComplainResponse
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\n", - " " - ] - }, - "metadata": {}, - "execution_count": 36 - } - ] - }, - { - "cell_type": "code", - "source": [ - "## há apenas duas observações com \"Marital_Status=YOLO\" e elas estão duplicadas.\n", - "## a única diferença entre elas é que uma tem \"Response=0\" e outra \"Response=1\". \n", - "## em outras palavras, uma diz que o cliente aceitou a última oferta de campanha e a outra diz que ele não aceitou, e isso nos leva a outro problema:\n", - "## identificar qual observação é a correta.\n", - "## para descobrir qual das duas está correta, primeiro vou verificar todas as dummies sobre as campanhas \n", - "## quero descobrir se todos os clientes aceitaram pelo menos uma das ofertas. \n", - "## se for o caso, irei manter a observação que aceitou a campanha. Se não, não posso apagar nenhuma das duas e irei seguir outro caminho de análise." - ], - "metadata": { - "id": "6aBslgWCVJXE" - }, - "execution_count": 37, - "outputs": [] - }, - { - "cell_type": "code", - "source": [ - "df[['ID', 'AcceptedCmp3', 'AcceptedCmp4', 'AcceptedCmp5', 'AcceptedCmp1', 'AcceptedCmp2', 'Response']]" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 0 - }, - "id": "pFwjS7nfoy5K", - "outputId": "64879fc5-f791-4bcc-ef80-76f0be8f9944" - }, - "execution_count": 38, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - " ID AcceptedCmp3 AcceptedCmp4 AcceptedCmp5 AcceptedCmp1 \\\n", - "0 5524 0 0 0 0 \n", - "1 2174 0 0 0 0 \n", - "2 4141 0 0 0 0 \n", - "3 6182 0 0 0 0 \n", - "4 5324 0 0 0 0 \n", - "... ... ... ... ... ... \n", - "2235 10870 0 0 0 0 \n", - "2236 4001 0 0 0 1 \n", - "2237 7270 0 1 0 0 \n", - "2238 8235 0 0 0 0 \n", - "2239 9405 0 0 0 0 \n", - "\n", - " AcceptedCmp2 Response \n", - "0 0 1 \n", - "1 0 0 \n", - "2 0 0 \n", - "3 0 0 \n", - "4 0 0 \n", - "... ... ... \n", - "2235 0 0 \n", - "2236 0 0 \n", - "2237 0 0 \n", - "2238 0 0 \n", - "2239 0 1 \n", - "\n", - "[2240 rows x 7 columns]" - ], - "text/html": [ - "\n", - "
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IDYear_BirthEducationMarital_StatusIncomeKidhomeTeenhomeDt_CustomerRecencyMntWines...NumCatalogPurchasesNumStorePurchasesNumWebVisitsMonthAcceptedCmp3AcceptedCmp4AcceptedCmp5AcceptedCmp1AcceptedCmp2ComplainResponse
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..................................................................
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\n", - " " - ] - }, - "metadata": {}, - "execution_count": 40 - } - ] - }, - { - "cell_type": "code", - "source": [ - "## com isso, identificamos que os 334 clientes que aceitaram a oferta da última campanha fizeram sua última nos últimos 99 dias.\n", - "## agora, vamos ver se os clientes que não aceitaram a oferta da última campanha compraram nesse prazo também.\n", - "df[(df['Response']==0)].sort_values(\"Recency\",ascending=True)" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 0 - }, - "id": "0nWIgCumXr-i", - "outputId": "12805a77-0064-4095-a725-b42ea0c57870" - }, - "execution_count": 41, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - " ID Year_Birth Education Marital_Status Income Kidhome \\\n", - "536 1991 1967.0 Graduation Together 44931.0 0 \n", - "1975 10311 1969.0 Graduation Married 4428.0 0 \n", - "758 10470 1979.0 Master Married 40662.0 1 \n", - "2049 2079 1947.0 2n Cycle Married 81044.0 0 \n", - "23 4047 1954.0 PhD Married 65324.0 0 \n", - "... ... ... ... ... ... ... \n", - "1685 7947 1969.0 Graduation Married 42231.0 1 \n", - "1894 1743 1974.0 Graduation Single 69719.0 0 \n", - "700 9977 1973.0 Graduation Divorced 78901.0 0 \n", - "208 868 1966.0 Graduation Married 44794.0 0 \n", - "725 7212 1966.0 Graduation Married 44794.0 0 \n", - "\n", - " Teenhome Dt_Customer Recency MntWines ... NumCatalogPurchases \\\n", - "536 1 2014-01-18 0 78 ... 1 \n", - "1975 1 2013-10-05 0 16 ... 0 \n", - "758 0 2013-03-15 0 40 ... 1 \n", - "2049 0 2013-12-27 0 450 ... 6 \n", - "23 1 2014-01-11 0 384 ... 2 \n", - "... ... ... ... ... ... ... \n", - "1685 1 2014-03-25 99 24 ... 0 \n", - "1894 0 2014-05-26 99 273 ... 3 \n", - "700 1 2013-09-17 99 321 ... 3 \n", - "208 1 2014-06-08 99 54 ... 0 \n", - "725 1 2014-06-08 99 54 ... 0 \n", - "\n", - " NumStorePurchases NumWebVisitsMonth AcceptedCmp3 AcceptedCmp4 \\\n", - "536 3 5 0 0 \n", - "1975 0 1 0 0 \n", - "758 3 4 0 0 \n", - "2049 10 1 0 0 \n", - "23 9 4 0 0 \n", - "... ... ... ... ... \n", - "1685 3 5 0 0 \n", - "1894 5 1 0 0 \n", - "700 5 4 0 0 \n", - "208 3 6 0 0 \n", - "725 3 6 0 0 \n", - "\n", - " AcceptedCmp5 AcceptedCmp1 AcceptedCmp2 Complain Response \n", - "536 0 0 0 0 0 \n", - "1975 0 0 0 0 0 \n", - "758 0 0 0 0 0 \n", - "2049 0 0 0 0 0 \n", - "23 0 0 0 0 0 \n", - "... ... ... ... ... ... \n", - "1685 0 0 0 0 0 \n", - "1894 0 0 0 0 0 \n", - "700 0 0 0 0 0 \n", - "208 0 0 0 0 0 \n", - "725 0 0 0 0 0 \n", - "\n", - "[1906 rows x 27 columns]" - ], - "text/html": [ - "\n", - "
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1906 rows × 27 columns

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\n", - " " - ] - }, - "metadata": {}, - "execution_count": 41 - } - ] - }, - { - "cell_type": "code", - "source": [ - "## também não conseguimos identificar qual dos dois cadastros é o correto porque dentre os 1906 clientes que não aceitaram a oferta, todos também compraram nos últimos 99 dias." - ], - "metadata": { - "id": "-mtKJto2caGa" - }, - "execution_count": 42, - "outputs": [] - }, - { - "cell_type": "code", - "source": [ - "## partindo para outra análise, vamos pensar nos dados do anunciado.\n", - "## de acordo com a questão do ifood, a receita total da última campanha foi de 3674 U.\n", - "## se considerarmos que 334 clientes (o yolo incluso) que aceitaram a última oferta gastaram um valor X em média, o que podemos inferir?\n", - "\n", - "gmed_por_cliente_yincluso=3674/334\n", - "gmed_por_cliente_yincluso\n" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "yVaOgEVgcre7", - "outputId": "f4bd1124-eed6-4c3f-befb-dab9ea6c37d8" - }, - "execution_count": 43, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "11.0" - ] - }, - "metadata": {}, - "execution_count": 43 - } - ] - }, - { - "cell_type": "code", - "source": [ - "## ok, 11 \"redondo\", um resultado interessante.\n", - "## e se considerarmos que na verdade o Yolo não comprou o produto da última oferta, e os 3674 U da campanha foram divididos entre 333 clientes? \n", - "## qual será o valor médio do gasto por cliente\n", - "## e lembrando que a variável que apagamos da receita por cliente era uma constante com valor igual a 11\n", - "## o que podemos inferir?\n", - "gmed_por_cliente_ynaoinc=3674/333\n", - "gmed_por_cliente_ynaoinc" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "7nOSN_SCipPC", - "outputId": "c4f4c598-7174-4226-ffd9-53bc5fd87ed3" - }, - "execution_count": 44, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "11.033033033033034" - ] - }, - "metadata": {}, - "execution_count": 44 - } - ] - }, - { - "cell_type": "code", - "source": [ - "## hmmm, achamos uma pista interessante. \n", - "## lembra que apagamos uma variável chamada \"Z_revenue\" que era uma constante=11 e que a definição dela no dicionário é \"revenue from the new gadget\"\n", - "## se considerarmos que essa variável indica a receita média por cliente na nova campanha, podemos dizer que o YOLO sendo um cliente que aceitou a campanha faz mais sentido\n", - "## do que o YOLO sendo um cliente que não aceitou a oferta, já que 11,03 é mais distante de 11 do que o próprio 11 redondo." - ], - "metadata": { - "id": "xcjkmfQpjbAG" - }, - "execution_count": 45, - "outputs": [] - }, - { - "cell_type": "code", - "source": [ - "## vamos agora, pensar pela ótica da taxa de sucesso da campanha.\n", - "## de acordo com nossas informações, a taxa foi de 15%.\n", - "## portanto, considerando que 334 clientes (yolo response=1) compraram o produto e que na verdade são 2239 clientes na base, temos:\n", - "tx_suc_yr1=334/2239\n", - "tx_suc_yr1" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "cOgp5vzDkSx_", - "outputId": "8d57fb24-43e1-4dac-cdb8-a46b2935189e" - }, - "execution_count": 46, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "0.14917373827601607" - ] - }, - "metadata": {}, - "execution_count": 46 - } - ] - }, - { - "cell_type": "code", - "source": [ - "## por outro lado, considerando 333 clientes (yolo response=0) e 2239 clientes, temos:\n", - "tx_suc_yr0=333/2239\n", - "tx_suc_yr0" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "U6c0tN-WlM7v", - "outputId": "e07b244b-d0ba-4056-9efa-5d361aed1999" - }, - "execution_count": 47, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "0.14872711031710584" - ] - }, - "metadata": {}, - "execution_count": 47 - } - ] - }, - { - "cell_type": "code", - "source": [ - "## como o primeiro resultado está mais próximo de 15% do que o segundo, podemos dizer que a hipótese de que o YOLO correto é o que comprou o produto está ainda mais forte.\n", - "## porém, ainda não é precisamente 15%, então melhor não tomar uma decisão ainda\n", - "## vamos fazer outra análise" - ], - "metadata": { - "id": "8Gto5otXlaOz" - }, - "execution_count": 48, - "outputs": [] - }, - { - "cell_type": "code", - "source": [ - "## minha próxima ideia é analisar todos os clientes que compraram nos útlimos 3 dias, nasceram em 1973 e tem PhD. Quem sabe a informação não está triplicada.\n", - "df[(df['Recency']==3) & (df['Education']=='PhD')& (df['Year_Birth']==1973)]" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 0 - }, - "id": "UtbIsCH-mfjf", - "outputId": "286efc49-b812-44ca-b4c3-5e61723aae4d" - }, - "execution_count": 49, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - " ID Year_Birth Education Marital_Status Income Kidhome Teenhome \\\n", - "1595 1041 1973.0 PhD Single 48432.0 0 1 \n", - "2177 492 1973.0 PhD YOLO 48432.0 0 1 \n", - "2202 11133 1973.0 PhD YOLO 48432.0 0 1 \n", - "\n", - " Dt_Customer Recency MntWines ... NumCatalogPurchases \\\n", - "1595 2012-10-18 3 322 ... 1 \n", - "2177 2012-10-18 3 322 ... 1 \n", - "2202 2012-10-18 3 322 ... 1 \n", - "\n", - " NumStorePurchases NumWebVisitsMonth AcceptedCmp3 AcceptedCmp4 \\\n", - "1595 6 8 0 0 \n", - "2177 6 8 0 0 \n", - "2202 6 8 0 0 \n", - "\n", - " AcceptedCmp5 AcceptedCmp1 AcceptedCmp2 Complain Response \n", - "1595 0 0 0 0 1 \n", - "2177 0 0 0 0 0 \n", - "2202 0 0 0 0 1 \n", - "\n", - "[3 rows x 27 columns]" - ], - "text/html": [ - "\n", - "
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IDYear_BirthEducationMarital_StatusIncomeKidhomeTeenhomeDt_CustomerRecencyMntWines...NumCatalogPurchasesNumStorePurchasesNumWebVisitsMonthAcceptedCmp3AcceptedCmp4AcceptedCmp5AcceptedCmp1AcceptedCmp2ComplainResponse
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\n", - " " - ] - }, - "metadata": {}, - "execution_count": 49 - } - ] - }, - { - "cell_type": "code", - "source": [ - "## BINGO!!!! a informação está triplicada. Os três clientes são a mesma pessoa, a diferença está que o novo ID (1041) tem o Marital_status=Single\n", - "## MASSSSSS...pesquisando no google, pude compilar o seguinte:\n", - "## 'YOLO:“you only live once” — a term that is widely seen to have been popularized by Canadian rapper Drake in 2011.\" \n", - "## \"used to express the view that one should make the most of the present moment without worrying about the future.\"\n", - "## ou seja, yolo é uma expressão popular americana que significa que só se vive uma vez.\n", - "## além disso, o termo é frequentemente utilizado na frase SOLO YOLO, que indica a filosofia de vida individualista.\n", - "\n", - "\n", - "## logoooo... podemos sim inferir que o ID 1041 em que a pessoa tem o estado civil de solteira e aceitou a última campanha faz sentido! \n", - "## portanto, o melhor a se fazer é deletar ambas as observações com o marital status YOLO." - ], - "metadata": { - "id": "AOOOaHcHol0J" - }, - "execution_count": 50, - "outputs": [] - }, - { - "cell_type": "code", - "source": [ - "## após essa longa batalha, vamos então deletar as observações erradas:\n", - "df.drop(index=[2177,2202],inplace=True)" - ], - "metadata": { - "id": "f6FQXXYeqmeG" - }, - "execution_count": 51, - "outputs": [] - }, - { - "cell_type": "code", - "source": [ - "## verificando se deu certo\n", - "df['Marital_Status'].unique()" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "htDUWsEqrX0l", - "outputId": "e180e37b-a1cd-4308-85e9-52a31ee7cffb" - }, - "execution_count": 52, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "array(['Single', 'Together', 'Married', 'Divorced', 'Widow', 'Alone',\n", - " 'Absurd'], dtype=object)" - ] - }, - "metadata": {}, - "execution_count": 52 - } - ] - }, - { - "cell_type": "code", - "source": [ - "df" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 0 - }, - "id": "kC81LKTrrkf9", - "outputId": "c4e6f28c-0410-4840-e73d-adec7e8ef15f" - }, - "execution_count": 53, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - " ID Year_Birth Education Marital_Status Income Kidhome \\\n", - "0 5524 1957.0 Graduation Single 58138.0 0 \n", - "1 2174 1954.0 Graduation Single 46344.0 1 \n", - "2 4141 1965.0 Graduation Together 71613.0 0 \n", - "3 6182 1984.0 Graduation Together 26646.0 1 \n", - "4 5324 1981.0 PhD Married 58293.0 1 \n", - "... ... ... ... ... ... ... \n", - "2235 10870 1967.0 Graduation Married 61223.0 0 \n", - "2236 4001 1946.0 PhD Together 64014.0 2 \n", - "2237 7270 1981.0 Graduation Divorced 56981.0 0 \n", - "2238 8235 1956.0 Master Together 69245.0 0 \n", - "2239 9405 1954.0 PhD Married 52869.0 1 \n", - "\n", - " Teenhome Dt_Customer Recency MntWines ... NumCatalogPurchases \\\n", - "0 0 2012-09-04 58 635 ... 10 \n", - "1 1 2014-03-08 38 11 ... 1 \n", - "2 0 2013-08-21 26 426 ... 2 \n", - "3 0 2014-02-10 26 11 ... 0 \n", - "4 0 2014-01-19 94 173 ... 3 \n", - "... ... ... ... ... ... ... \n", - "2235 1 2013-06-13 46 709 ... 3 \n", - "2236 1 2014-06-10 56 406 ... 2 \n", - "2237 0 2014-01-25 91 908 ... 3 \n", - "2238 1 2014-01-24 8 428 ... 5 \n", - "2239 1 2012-10-15 40 84 ... 1 \n", - "\n", - " NumStorePurchases NumWebVisitsMonth AcceptedCmp3 AcceptedCmp4 \\\n", - "0 4 7 0 0 \n", - "1 2 5 0 0 \n", - "2 10 4 0 0 \n", - "3 4 6 0 0 \n", - "4 6 5 0 0 \n", - "... ... ... ... ... \n", - "2235 4 5 0 0 \n", - "2236 5 7 0 0 \n", - "2237 13 6 0 1 \n", - "2238 10 3 0 0 \n", - "2239 4 7 0 0 \n", - "\n", - " AcceptedCmp5 AcceptedCmp1 AcceptedCmp2 Complain Response \n", - "0 0 0 0 0 1 \n", - "1 0 0 0 0 0 \n", - "2 0 0 0 0 0 \n", - "3 0 0 0 0 0 \n", - "4 0 0 0 0 0 \n", - "... ... ... ... ... ... \n", - "2235 0 0 0 0 0 \n", - "2236 0 1 0 0 0 \n", - "2237 0 0 0 0 0 \n", - "2238 0 0 0 0 0 \n", - "2239 0 0 0 0 1 \n", - "\n", - "[2238 rows x 27 columns]" - ], - "text/html": [ - "\n", - "
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361821984.0GraduationTogether26646.0102014-02-102611...0460000000
453241981.0PhDMarried58293.0102014-01-1994173...3650000000
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2235108701967.0GraduationMarried61223.0012013-06-1346709...3450000000
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121741954.0GraduationSingle46344.0112014-03-083811...1250000000
241411965.0GraduationTogether71613.0002013-08-2126426...21040000000
361821984.0GraduationTogether26646.0102014-02-102611...0460000000
453241981.0PhDMarried58293.0102014-01-1994173...3650000000
..................................................................
2235108701967.0GraduationMarried61223.0012013-06-1346709...3450000000
223640011946.0PhDTogether64014.0212014-06-1056406...2570001000
223772701981.0GraduationDivorced56981.0002014-01-2591908...31360100000
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\n", - " " - ] - }, - "metadata": {}, - "execution_count": 70 - } - ] - }, - { - "cell_type": "code", - "source": [ - "## ok, o ID 433 é único. Agora vamos pro próximo, ID 7660" - ], - "metadata": { - "id": "l4h5KfsNBwMG" - }, - "execution_count": 71, - "outputs": [] - }, - { - "cell_type": "code", - "source": [ - "df[(df['Dt_Customer']=='2014-05-19')&(df['Year_Birth']==1973)]" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 0 - }, - "id": "PH8vCHYVBJnM", - "outputId": "7cebe2b1-4232-4f91-a780-605a29ad8fcc" - }, - "execution_count": 72, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - " ID Year_Birth Education Marital_Status Income Kidhome Teenhome \\\n", - "138 7660 1973.0 PhD Alone 35860.0 1 1 \n", - "1063 2055 1973.0 PhD Divorced 35860.0 1 1 \n", - "1260 5107 1973.0 PhD Divorced 35860.0 1 1 \n", - "1585 1626 1973.0 PhD Divorced 35860.0 1 1 \n", - "\n", - " Dt_Customer Recency MntWines ... NumCatalogPurchases \\\n", - "138 2014-05-19 37 15 ... 1 \n", - "1063 2014-05-19 37 15 ... 1 \n", - "1260 2014-05-19 37 15 ... 1 \n", - "1585 2014-05-19 37 15 ... 1 \n", - "\n", - " NumStorePurchases NumWebVisitsMonth AcceptedCmp3 AcceptedCmp4 \\\n", - "138 2 5 1 0 \n", - "1063 2 5 1 0 \n", - "1260 2 5 1 0 \n", - "1585 2 5 1 0 \n", - "\n", - " AcceptedCmp5 AcceptedCmp1 AcceptedCmp2 Complain Response \n", - "138 0 0 0 0 1 \n", - "1063 0 0 0 0 0 \n", - "1260 0 0 0 0 0 \n", - "1585 0 0 0 0 1 \n", - "\n", - "[4 rows x 27 columns]" - ], - "text/html": [ - "\n", - "
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IDYear_BirthEducationMarital_StatusIncomeKidhomeTeenhomeDt_CustomerRecencyMntWines...NumCatalogPurchasesNumStorePurchasesNumWebVisitsMonthAcceptedCmp3AcceptedCmp4AcceptedCmp5AcceptedCmp1AcceptedCmp2ComplainResponse
13876601973.0PhDAlone35860.0112014-05-193715...1251000001
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241411965.0GraduationTogether71613.0002013-08-2126426...21040000000
361821984.0GraduationTogether26646.0102014-02-102611...0460000000
453241981.0PhDMarried58293.0102014-01-1994173...3650000000
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2235108701967.0GraduationMarried61223.0012013-06-1346709...3450000000
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\n", - " " - ] - }, - "metadata": {}, - "execution_count": 82 - } - ] - }, - { - "cell_type": "code", - "source": [ - "## Analisando esses dados, acredito que o cliente é bem representado tanto pelo grupo de divorciados quanto pelo de víuvos, mas em uma análise bem detalhada é possível perceber que \n", - "## entre os dois grupos suas características de consumo são mais semelhantes às do grupo de divorciados, apesar de a renda média do grupo de viúvos ser mais próxima à deste cliente.\n", - "## Logo, vou substituir o estado civil para divorced\n", - "df.loc[df['Marital_Status']=='Alone','Marital_Status']='Divorced'\n", - "df[df['ID']==433]" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 0 - }, - "id": "yai55n6tLp8O", - "outputId": "3326d35d-6a0f-40cb-e177-7e62974552c3" - }, - "execution_count": 83, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - " ID Year_Birth Education Marital_Status Income Kidhome Teenhome \\\n", - "131 433 1958.0 Master Divorced 61331.0 1 1 \n", - "\n", - " Dt_Customer Recency MntWines ... NumCatalogPurchases \\\n", - "131 2013-03-10 42 534 ... 1 \n", - "\n", - " NumStorePurchases NumWebVisitsMonth AcceptedCmp3 AcceptedCmp4 \\\n", - "131 6 8 0 0 \n", - "\n", - " AcceptedCmp5 AcceptedCmp1 AcceptedCmp2 Complain Response \n", - "131 0 0 0 0 0 \n", - "\n", - "[1 rows x 27 columns]" - ], - "text/html": [ - "\n", - "
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Income
Marital_StatusEducation
DivorcedBasic9548.00
Graduation54526.04
Master50159.03
PhD53786.08
MarriedBasic21960.50
Graduation50800.26
Master50686.06
PhD58138.03
SingleBasic18238.67
Graduation51435.23
Master53577.06
PhD53314.61
TogetherBasic21240.07
Graduation53607.40
Master49495.94
PhD56041.42
WidowBasic22123.00
Graduation54976.66
Master56211.12
PhD60288.08
\n", - "
\n", - " \n", - " \n", - " \n", - "\n", - " \n", - "
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\n", - " " - ] - }, - "metadata": {}, - "execution_count": 89 - } - ] - }, - { - "cell_type": "code", - "source": [ - "## Substituindo as 24 observações com renda nula manualmente:\n", - "## Single\n", - "df.loc[((df['Income'].isnull()) & (df['Marital_Status']=='Single') & (df['Education']=='Graduation')),'Income']= 51435.23\n", - "df.loc[((df['Income'].isnull()) & (df['Marital_Status']=='Single') & (df['Education']=='PhD')),'Income']= 53314.61\n", - "df.loc[((df['Income'].isnull()) & (df['Marital_Status']=='Single') & (df['Education']=='Master')),'Income']= 53577.06\n", - "## Married\n", - "df.loc[((df['Income'].isnull()) & (df['Marital_Status']=='Married') & (df['Education']=='Graduation')),'Income']= 50800.26\n", - "df.loc[((df['Income'].isnull()) & (df['Marital_Status']=='Married') & (df['Education']=='PhD')),'Income']= 58138.03\n", - "df.loc[((df['Income'].isnull()) & (df['Marital_Status']=='Married') & (df['Education']=='Master')),'Income']= 50686.06\n", - "## Together\n", - "df.loc[((df['Income'].isnull()) & (df['Marital_Status']=='Together') & (df['Education']=='Graduation')),'Income']= 53607.40\n", - "df.loc[((df['Income'].isnull()) & (df['Marital_Status']=='Together') & (df['Education']=='PhD')),'Income']= 56041.42\n", - "df.loc[((df['Income'].isnull()) & (df['Marital_Status']=='Together') & (df['Education']=='Master')),'Income']= 49495.94\n", - "## Widow\n", - "df.loc[((df['Income'].isnull()) & (df['Marital_Status']=='Widow') & (df['Education']=='Master')),'Income']= 56211.12" - ], - "metadata": { - "id": "KAGRDsBqVTqi" - }, - "execution_count": 90, - "outputs": [] - }, - { - "cell_type": "code", - "source": [ - "## Provavelmenter tem uma forma mais eficiente de fazer esse comando usando o if, o for e o where, mas eu não consegui fazer.\n", - "## Vou tirar a dúvida depois com algum mentor da Awari.\n", - "## Verificando se deu certo\n", - "df['Income'].isnull().value_counts()" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "d9GMkhZiY3d7", - "outputId": "7db2cf8e-fbb5-456f-b94d-02d61272a2a1" - }, - "execution_count": 91, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "False 2233\n", - "Name: Income, dtype: int64" - ] - }, - "metadata": {}, - "execution_count": 91 - } - ] - }, - { - "cell_type": "markdown", - "source": [ - "##### **Forma 2**" - ], - "metadata": { - "id": "lz5LZSWnq6LJ" - } - }, - { - "cell_type": "markdown", - "source": [ - "A forma dois consiste no seguinte: queremos substituir os dados nulos da renda de algumas observações por estimativas mais precisas oriundas de uma regressão linear ou linear com grau polinomial transformado. Para isso:\n", - "\n", - "1. Precisamos identificar quais seriam as variáveis ideias em um modelo para estimar a renda.\n", - "\n", - "2. Para descobrir essas variáveis, iremos analisar a correlação entre a renda e as variáveis disponíveis.\n", - "\n", - "3. Porém, algumas variáveis que podem ser relevantes ao modelo são categóricas e estão em formato de texto (Marital Status e Education). Nesses casos, precisamos transformar as strings em números para captar seus efeitos. Podemos fazer isso transformando a categoria em uma dummie ou em uma categoria codificada (encoding). \n", - "\n", - "4. Vai ser mais longo, mas para fazer um trabalho mais completo vou aplicar ambas as metodologias e adotar a que tiver o melhor resultado em termos de correlação, sendo válidos os seguintes graus de correlação:\n", - "\n", - "- \"Perfect: If the value is near ± 1, then it said to be a perfect correlation: as one variable increases, the other variable tends to also increase (if positive) or decrease (if negative).\n", - "\n", - "- High degree: If the coefficient value lies between ± 0.50 and ± 1, then it is said to be a strong correlation.\n", - "\n", - "- Moderate degree: If the value lies between ± 0.30 and ± 0.49, then it is said to be a medium correlation.\n", - "\n", - "- Low degree: When the value lies below + .29, then it is said to be a small correlation.\n", - "\n", - "- No correlation: When the value is zero.\"\n", - "\n", - "5. Definidas as variáveis com correlação aceitável para o modelo, vou salvá-las juntamente com suas correlações em um dataframe e depois vou estimar a regressão linear em bases separadas para treino e validação.\n", - "\n", - "6. Se a regressão linear não apresentar resultados satisfatórios de R² e erro, vou estimar uma regressão múltipla.\n", - "\n", - "7. Feito tudo isso, vou escolher o melhor modelo, estimar a regressão para o caso real e substituir os nulos." - ], - "metadata": { - "id": "JNxnzXdlrnKL" - } - }, - { - "cell_type": "code", - "source": [ - "## Verificando se no df_copy as observações continuam com renda nula\n", - "df_copy[df_copy['Income'].isnull()]" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 927 - }, - "id": "ASmMnFWbo-G6", - "outputId": "fca6a497-f4cb-4262-8e6b-9ac000274acd" - }, - "execution_count": 92, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - " ID Year_Birth Education Marital_Status Income Kidhome Teenhome \\\n", - "10 1994 1983.0 Graduation Married NaN 1 0 \n", - "27 5255 1986.0 Graduation Single NaN 1 0 \n", - "43 7281 1959.0 PhD Single NaN 0 0 \n", - "48 7244 1951.0 Graduation Single NaN 2 1 \n", - "58 8557 1982.0 Graduation Single NaN 1 0 \n", - "71 10629 1973.0 Master Married NaN 1 0 \n", - "90 8996 1957.0 PhD Married NaN 2 1 \n", - "91 9235 1957.0 Graduation Single NaN 1 1 \n", - "92 5798 1973.0 Master Together NaN 0 0 \n", - "128 8268 1961.0 PhD Married NaN 0 1 \n", - "133 1295 1963.0 Graduation Married NaN 0 1 \n", - "312 2437 1989.0 Graduation Married NaN 0 0 \n", - "319 2863 1970.0 Graduation Single NaN 1 2 \n", - "1379 10475 1970.0 Master Together NaN 0 1 \n", - "1382 2902 1958.0 Graduation Together NaN 1 1 \n", - "1383 4345 1964.0 Master Single NaN 1 1 \n", - "1386 3769 1972.0 PhD Together NaN 1 0 \n", - "2059 7187 1969.0 Master Together NaN 1 1 \n", - "2061 1612 1981.0 PhD Single NaN 1 0 \n", - "2078 5079 1971.0 Graduation Married NaN 1 1 \n", - "2079 10339 1954.0 Master Together NaN 0 1 \n", - "2081 3117 1955.0 Graduation Single NaN 0 1 \n", - "2084 5250 1943.0 Master Widow NaN 0 0 \n", - "2228 8720 1978.0 Master Together NaN 0 0 \n", - "2233 9432 1977.0 Graduation Together NaN 1 0 \n", - "\n", - " Dt_Customer Recency MntWines ... NumCatalogPurchases \\\n", - "10 2013-11-15 11 5 ... 0 \n", - "27 2013-02-20 19 5 ... 0 \n", - "43 2013-11-05 80 81 ... 3 \n", - "48 2014-01-01 96 48 ... 1 \n", - "58 2013-06-17 57 11 ... 0 \n", - "71 2012-09-14 25 25 ... 0 \n", - "90 2012-11-19 4 230 ... 2 \n", - "91 2014-05-27 45 7 ... 0 \n", - "92 2013-11-23 87 445 ... 4 \n", - "128 2013-07-11 23 352 ... 1 \n", - "133 2013-08-11 96 231 ... 5 \n", - "312 2013-06-03 69 861 ... 5 \n", - "319 2013-08-23 67 738 ... 3 \n", - "1379 2013-04-01 39 187 ... 2 \n", - "1382 2012-09-03 87 19 ... 0 \n", - "1383 2014-01-12 49 5 ... 0 \n", - "1386 2014-03-02 17 25 ... 0 \n", - "2059 2013-05-18 52 375 ... 10 \n", - "2061 2013-05-31 82 23 ... 0 \n", - "2078 2013-03-03 82 71 ... 1 \n", - "2079 2013-06-23 83 161 ... 1 \n", - "2081 2013-10-18 95 264 ... 1 \n", - "2084 2013-10-30 75 532 ... 5 \n", - "2228 2012-08-12 53 32 ... 0 \n", - "2233 2013-06-02 23 9 ... 1 \n", - "\n", - " NumStorePurchases NumWebVisitsMonth AcceptedCmp3 AcceptedCmp4 \\\n", - "10 2 7 0 0 \n", - "27 0 1 0 0 \n", - "43 4 2 0 0 \n", - "48 4 6 0 0 \n", - "58 3 6 0 0 \n", - "71 3 8 0 0 \n", - "90 8 9 0 0 \n", - "91 2 7 0 0 \n", - "92 8 1 0 0 \n", - "128 7 6 0 0 \n", - "133 7 4 0 0 \n", - "312 12 3 0 1 \n", - "319 10 7 0 1 \n", - "1379 6 5 0 0 \n", - "1382 3 5 0 0 \n", - "1383 2 7 0 0 \n", - "1386 3 7 0 0 \n", - "2059 4 3 0 0 \n", - "2061 3 6 0 0 \n", - "2078 3 8 0 0 \n", - "2079 4 6 0 0 \n", - "2081 5 7 0 0 \n", - "2084 11 1 0 0 \n", - "2228 1 0 0 1 \n", - "2233 3 6 0 0 \n", - "\n", - " AcceptedCmp5 AcceptedCmp1 AcceptedCmp2 Complain Response \n", - "10 0 0 0 0 0 \n", - "27 0 0 0 0 0 \n", - "43 0 0 0 0 0 \n", - "48 0 0 0 0 0 \n", - "58 0 0 0 0 0 \n", - "71 0 0 0 0 0 \n", - "90 0 0 0 0 0 \n", - "91 0 0 0 0 0 \n", - "92 0 0 0 0 0 \n", - "128 0 0 0 0 0 \n", - "133 0 0 0 0 0 \n", - "312 0 1 0 0 0 \n", - "319 0 1 0 0 0 \n", - "1379 0 0 0 0 0 \n", - "1382 0 0 0 0 0 \n", - "1383 0 0 0 0 0 \n", - "1386 0 0 0 0 0 \n", - "2059 0 0 0 0 0 \n", - "2061 0 0 0 0 0 \n", - "2078 0 0 0 0 0 \n", - "2079 0 0 0 0 0 \n", - "2081 0 0 0 0 0 \n", - "2084 1 0 0 0 1 \n", - "2228 0 0 0 0 0 \n", - "2233 0 0 0 0 0 \n", - "\n", - "[25 rows x 27 columns]" - ], - "text/html": [ - "\n", - "
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IDYear_BirthEducationMarital_StatusIncomeKidhomeTeenhomeDt_CustomerRecencyMntWines...ResponseEc_DivorcedEc_MarriedEc_SingleEc_TogetherEc_WidowEd_BasicEd_GraduationEd_MasterEd_PhD
055241957.0GraduationSingle58138.0002012-09-0458635...1001000100
121741954.0GraduationSingle46344.0112014-03-083811...0001000100
241411965.0GraduationTogether71613.0002013-08-2126426...0000100100
361821984.0GraduationTogether26646.0102014-02-102611...0000100100
453241981.0PhDMarried58293.0102014-01-1994173...0010000001
..................................................................
2235108701967.0GraduationMarried61223.0012013-06-1346709...0010000100
223640011946.0PhDTogether64014.0212014-06-1056406...0000100001
223772701981.0GraduationDivorced56981.0002014-01-2591908...0100000100
223882351956.0MasterTogether69245.0012014-01-248428...0000100010
223994051954.0PhDMarried52869.0112012-10-154084...1010000001
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2233 rows × 36 columns

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NumCatalogPurchases NumStorePurchases \\\n", - "Income ... 0.70 0.63 \n", - "Ec_Married ... -0.01 0.01 \n", - "Ec_Single ... -0.01 -0.02 \n", - "Ec_Together ... 0.00 -0.01 \n", - "Ec_Widow ... 0.04 0.04 \n", - "Ed_Basic ... -0.12 -0.14 \n", - "Ed_Graduation ... 0.02 0.01 \n", - "Ed_Master ... -0.04 -0.01 \n", - "Ed_PhD ... 0.06 0.05 \n", - "Year_Birth ... -0.12 -0.14 \n", - "Kidhome ... -0.50 -0.50 \n", - "Teenhome ... -0.11 0.05 \n", - "Recency ... 0.02 -0.00 \n", - "MntWines ... 0.64 0.64 \n", - "MntFruits ... 0.49 0.46 \n", - "MntMeatProducts ... 0.72 0.48 \n", - "MntFishProducts ... 0.53 0.46 \n", - "MntSweetProducts ... 0.49 0.45 \n", - "MntGoldProds ... 0.44 0.38 \n", - "NumDealsPurchases ... -0.01 0.07 \n", - "NumWebPurchases ... 0.38 0.50 \n", - "NumCatalogPurchases ... 1.00 0.52 \n", - "NumStorePurchases ... 0.52 1.00 \n", - "NumWebVisitsMonth ... -0.52 -0.43 \n", - "AcceptedCmp3 ... 0.11 -0.06 \n", - "AcceptedCmp4 ... 0.14 0.18 \n", - "AcceptedCmp5 ... 0.32 0.21 \n", - "AcceptedCmp1 ... 0.31 0.18 \n", - "AcceptedCmp2 ... 0.10 0.09 \n", - "Complain ... -0.02 -0.02 \n", - "Response ... 0.22 0.04 \n", - "\n", - " NumWebVisitsMonth AcceptedCmp3 AcceptedCmp4 \\\n", - "Income -0.65 -0.01 0.22 \n", - "Ec_Married 0.02 0.00 -0.01 \n", - "Ec_Single -0.01 0.01 -0.01 \n", - "Ec_Together -0.01 -0.02 -0.00 \n", - "Ec_Widow -0.03 -0.01 0.04 \n", - "Ed_Basic 0.10 0.02 -0.04 \n", - "Ed_Graduation -0.01 -0.01 -0.01 \n", - "Ed_Master -0.01 -0.01 -0.01 \n", - "Ed_PhD -0.01 0.01 0.04 \n", - "Year_Birth 0.12 0.06 -0.06 \n", - "Kidhome 0.45 0.01 -0.16 \n", - "Teenhome 0.13 -0.05 0.04 \n", - "Recency -0.02 -0.03 0.02 \n", - "MntWines -0.32 0.07 0.37 \n", - "MntFruits -0.42 0.02 0.01 \n", - "MntMeatProducts -0.54 0.02 0.10 \n", - "MntFishProducts -0.45 0.00 0.02 \n", - "MntSweetProducts -0.42 0.00 0.03 \n", - "MntGoldProds -0.25 0.13 0.02 \n", - "NumDealsPurchases 0.35 -0.02 0.02 \n", - "NumWebPurchases -0.06 0.05 0.16 \n", - "NumCatalogPurchases -0.52 0.11 0.14 \n", - "NumStorePurchases -0.43 -0.06 0.18 \n", - "NumWebVisitsMonth 1.00 0.06 -0.03 \n", - "AcceptedCmp3 0.06 1.00 -0.08 \n", - "AcceptedCmp4 -0.03 -0.08 1.00 \n", - "AcceptedCmp5 -0.28 0.08 0.31 \n", - "AcceptedCmp1 -0.19 0.10 0.25 \n", - "AcceptedCmp2 -0.01 0.07 0.29 \n", - "Complain 0.02 0.01 -0.03 \n", - "Response -0.00 0.25 0.18 \n", - "\n", - " AcceptedCmp5 AcceptedCmp1 AcceptedCmp2 Complain \\\n", - "Income 0.40 0.33 0.10 -0.03 \n", - "Ec_Married 0.01 0.03 -0.04 -0.00 \n", - "Ec_Single -0.01 0.00 -0.01 0.02 \n", - "Ec_Together 0.01 -0.02 0.04 -0.00 \n", - "Ec_Widow 0.02 0.00 -0.00 -0.02 \n", - "Ed_Basic -0.04 -0.04 -0.02 -0.02 \n", - "Ed_Graduation 0.01 0.03 0.01 0.03 \n", - "Ed_Master -0.01 -0.02 -0.03 0.01 \n", - "Ed_PhD 0.02 -0.00 0.03 -0.04 \n", - "Year_Birth 0.02 -0.01 -0.01 -0.00 \n", - "Kidhome -0.21 -0.17 -0.08 0.04 \n", - "Teenhome -0.19 -0.14 -0.02 0.00 \n", - "Recency -0.00 -0.02 -0.00 0.01 \n", - "MntWines 0.47 0.35 0.21 -0.04 \n", - "MntFruits 0.22 0.19 -0.01 -0.01 \n", - "MntMeatProducts 0.37 0.31 0.04 -0.02 \n", - "MntFishProducts 0.20 0.26 0.00 -0.02 \n", - "MntSweetProducts 0.26 0.24 0.01 -0.02 \n", - "MntGoldProds 0.18 0.17 0.05 -0.03 \n", - "NumDealsPurchases -0.18 -0.12 -0.04 0.00 \n", - "NumWebPurchases 0.14 0.16 0.03 -0.02 \n", - "NumCatalogPurchases 0.32 0.31 0.10 -0.02 \n", - "NumStorePurchases 0.21 0.18 0.09 -0.02 \n", - "NumWebVisitsMonth -0.28 -0.19 -0.01 0.02 \n", - "AcceptedCmp3 0.08 0.10 0.07 0.01 \n", - "AcceptedCmp4 0.31 0.25 0.29 -0.03 \n", - "AcceptedCmp5 1.00 0.40 0.22 -0.01 \n", - "AcceptedCmp1 0.40 1.00 0.18 -0.03 \n", - "AcceptedCmp2 0.22 0.18 1.00 -0.01 \n", - "Complain -0.01 -0.03 -0.01 1.00 \n", - "Response 0.33 0.30 0.17 -0.00 \n", - "\n", - " Response \n", - "Income 0.16 \n", - "Ec_Married -0.08 \n", - "Ec_Single 0.11 \n", - "Ec_Together -0.07 \n", - "Ec_Widow 0.05 \n", - "Ed_Basic -0.05 \n", - "Ed_Graduation -0.04 \n", - "Ed_Master -0.02 \n", - "Ed_PhD 0.08 \n", - "Year_Birth 0.02 \n", - "Kidhome -0.08 \n", - "Teenhome -0.16 \n", - "Recency -0.20 \n", - "MntWines 0.25 \n", - "MntFruits 0.13 \n", - "MntMeatProducts 0.24 \n", - "MntFishProducts 0.11 \n", - "MntSweetProducts 0.12 \n", - "MntGoldProds 0.14 \n", - "NumDealsPurchases 0.00 \n", - "NumWebPurchases 0.15 \n", - "NumCatalogPurchases 0.22 \n", - "NumStorePurchases 0.04 \n", - "NumWebVisitsMonth -0.00 \n", - "AcceptedCmp3 0.25 \n", - "AcceptedCmp4 0.18 \n", - "AcceptedCmp5 0.33 \n", - "AcceptedCmp1 0.30 \n", - "AcceptedCmp2 0.17 \n", - "Complain -0.00 \n", - "Response 1.00 \n", - "\n", - "[31 rows x 31 columns]" - ], - "text/html": [ - "\n", - "
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IncomeEc_MarriedEc_SingleEc_TogetherEc_WidowEd_BasicEd_GraduationEd_MasterEd_PhDYear_Birth...NumCatalogPurchasesNumStorePurchasesNumWebVisitsMonthAcceptedCmp3AcceptedCmp4AcceptedCmp5AcceptedCmp1AcceptedCmp2ComplainResponse
Income1.00-0.01-0.020.000.04-0.230.01-0.030.10-0.20...0.700.63-0.65-0.010.220.400.330.10-0.030.16
Ec_Married-0.011.00-0.42-0.47-0.15-0.01-0.00-0.000.010.05...-0.010.010.020.00-0.010.010.03-0.04-0.00-0.08
Ec_Single-0.02-0.421.00-0.31-0.100.050.02-0.03-0.010.13...-0.01-0.02-0.010.01-0.01-0.010.00-0.010.020.11
Ec_Together0.00-0.47-0.311.00-0.11-0.00-0.010.03-0.02-0.05...0.00-0.01-0.01-0.02-0.000.01-0.020.04-0.00-0.07
Ec_Widow0.04-0.15-0.10-0.111.00-0.01-0.02-0.020.04-0.17...0.040.04-0.03-0.010.040.020.00-0.00-0.020.05
Ed_Basic-0.23-0.010.05-0.00-0.011.00-0.16-0.09-0.080.12...-0.12-0.140.100.02-0.04-0.04-0.04-0.02-0.02-0.05
Ed_Graduation0.01-0.000.02-0.01-0.02-0.161.00-0.59-0.530.06...0.020.01-0.01-0.01-0.010.010.030.010.03-0.04
Ed_Master-0.03-0.00-0.030.03-0.02-0.09-0.591.00-0.310.00...-0.04-0.01-0.01-0.01-0.01-0.01-0.02-0.030.01-0.02
Ed_PhD0.100.01-0.01-0.020.04-0.08-0.53-0.311.00-0.12...0.060.05-0.010.010.040.02-0.000.03-0.040.08
Year_Birth-0.200.050.13-0.05-0.170.120.060.00-0.121.00...-0.12-0.140.120.06-0.060.02-0.01-0.01-0.000.02
Kidhome-0.510.020.020.01-0.070.050.000.02-0.040.23...-0.50-0.500.450.01-0.16-0.21-0.17-0.080.04-0.08
Teenhome0.040.01-0.100.030.05-0.12-0.02-0.020.09-0.36...-0.110.050.13-0.050.04-0.19-0.14-0.020.00-0.16
Recency0.01-0.020.010.02-0.00-0.000.03-0.03-0.01-0.02...0.02-0.00-0.02-0.030.02-0.00-0.02-0.000.01-0.20
MntWines0.69-0.01-0.020.000.04-0.14-0.06-0.030.16-0.16...0.640.64-0.320.070.370.470.350.21-0.040.25
MntFruits0.51-0.010.01-0.010.03-0.060.11-0.03-0.08-0.01...0.490.46-0.420.020.010.220.19-0.01-0.010.13
MntMeatProducts0.69-0.020.030.000.02-0.110.06-0.030.01-0.03...0.720.48-0.540.020.100.370.310.04-0.020.24
MntFishProducts0.52-0.030.010.020.05-0.060.10-0.00-0.10-0.04...0.530.46-0.450.000.020.200.260.00-0.020.11
MntSweetProducts0.52-0.010.00-0.010.05-0.060.10-0.02-0.09-0.02...0.490.45-0.420.000.030.260.240.01-0.020.12
MntGoldProds0.39-0.020.00-0.010.05-0.060.13-0.02-0.12-0.06...0.440.38-0.250.130.020.180.170.05-0.030.14
NumDealsPurchases-0.110.03-0.050.000.00-0.04-0.010.010.01-0.07...-0.010.070.35-0.020.02-0.18-0.12-0.040.000.00
NumWebPurchases0.460.00-0.04-0.000.04-0.120.01-0.030.07-0.15...0.380.50-0.060.050.160.140.160.03-0.020.15
NumCatalogPurchases0.70-0.01-0.010.000.04-0.120.02-0.040.06-0.12...1.000.52-0.520.110.140.320.310.10-0.020.22
NumStorePurchases0.630.01-0.02-0.010.04-0.140.01-0.010.05-0.14...0.521.00-0.43-0.060.180.210.180.09-0.020.04
NumWebVisitsMonth-0.650.02-0.01-0.01-0.030.10-0.01-0.01-0.010.12...-0.52-0.431.000.06-0.03-0.28-0.19-0.010.02-0.00
AcceptedCmp3-0.010.000.01-0.02-0.010.02-0.01-0.010.010.06...0.11-0.060.061.00-0.080.080.100.070.010.25
AcceptedCmp40.22-0.01-0.01-0.000.04-0.04-0.01-0.010.04-0.06...0.140.18-0.03-0.081.000.310.250.29-0.030.18
AcceptedCmp50.400.01-0.010.010.02-0.040.01-0.010.020.02...0.320.21-0.280.080.311.000.400.22-0.010.33
AcceptedCmp10.330.030.00-0.020.00-0.040.03-0.02-0.00-0.01...0.310.18-0.190.100.250.401.000.18-0.030.30
AcceptedCmp20.10-0.04-0.010.04-0.00-0.020.01-0.030.03-0.01...0.100.09-0.010.070.290.220.181.00-0.010.17
Complain-0.03-0.000.02-0.00-0.02-0.020.030.01-0.04-0.00...-0.02-0.020.020.01-0.03-0.01-0.03-0.011.00-0.00
Response0.16-0.080.11-0.070.05-0.05-0.04-0.020.080.02...0.220.04-0.000.250.180.330.300.17-0.001.00
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IDYear_BirthEducationMarital_StatusIncomeKidhomeTeenhomeDt_CustomerRecencyMntWines...AcceptedCmp5AcceptedCmp1AcceptedCmp2ComplainResponseEc_DivorcedEc_MarriedEc_SingleEc_TogetherEc_Widow
055241957.01Single58138.0002012-09-0458635...0000100100
121741954.01Single46344.0112014-03-083811...0000000100
241411965.01Together71613.0002013-08-2126426...0000000010
361821984.01Together26646.0102014-02-102611...0000000010
453241981.03Married58293.0102014-01-1994173...0000001000
..................................................................
2235108701967.01Married61223.0012013-06-1346709...0000001000
223640011946.03Together64014.0212014-06-1056406...0100000010
223772701981.01Divorced56981.0002014-01-2591908...0000010000
223882351956.02Together69245.0012014-01-248428...0000000010
223994051954.03Married52869.0112012-10-154084...0000101000
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2233 rows × 32 columns

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AcceptedCmp4 AcceptedCmp5 \\\n", - "Income 0.52 ... 0.22 0.40 \n", - "Education -0.09 ... 0.04 0.02 \n", - "Year_Birth -0.04 ... -0.06 0.02 \n", - "Kidhome -0.39 ... -0.16 -0.21 \n", - "Teenhome -0.20 ... 0.04 -0.19 \n", - "Recency -0.00 ... 0.02 -0.00 \n", - "MntWines 0.40 ... 0.37 0.47 \n", - "MntFruits 0.59 ... 0.01 0.22 \n", - "MntMeatProducts 0.57 ... 0.10 0.37 \n", - "MntFishProducts 1.00 ... 0.02 0.20 \n", - "MntSweetProducts 0.58 ... 0.03 0.26 \n", - "MntGoldProds 0.42 ... 0.02 0.18 \n", - "NumDealsPurchases -0.14 ... 0.02 -0.18 \n", - "NumWebPurchases 0.29 ... 0.16 0.14 \n", - "NumCatalogPurchases 0.53 ... 0.14 0.32 \n", - "NumStorePurchases 0.46 ... 0.18 0.21 \n", - "NumWebVisitsMonth -0.45 ... -0.03 -0.28 \n", - "AcceptedCmp3 0.00 ... -0.08 0.08 \n", - "AcceptedCmp4 0.02 ... 1.00 0.31 \n", - "AcceptedCmp5 0.20 ... 0.31 1.00 \n", - "AcceptedCmp1 0.26 ... 0.25 0.40 \n", - "AcceptedCmp2 0.00 ... 0.29 0.22 \n", - "Complain -0.02 ... -0.03 -0.01 \n", - "Response 0.11 ... 0.18 0.33 \n", - "Ec_Married -0.03 ... -0.01 0.01 \n", - "Ec_Single 0.01 ... -0.01 -0.01 \n", - "Ec_Together 0.02 ... -0.00 0.01 \n", - "Ec_Widow 0.05 ... 0.04 0.02 \n", - "\n", - " AcceptedCmp1 AcceptedCmp2 Complain Response \\\n", - "Income 0.33 0.10 -0.03 0.16 \n", - "Education -0.01 0.02 -0.03 0.08 \n", - "Year_Birth -0.01 -0.01 -0.00 0.02 \n", - "Kidhome -0.17 -0.08 0.04 -0.08 \n", - "Teenhome -0.14 -0.02 0.00 -0.16 \n", - "Recency -0.02 -0.00 0.01 -0.20 \n", - "MntWines 0.35 0.21 -0.04 0.25 \n", - "MntFruits 0.19 -0.01 -0.01 0.13 \n", - "MntMeatProducts 0.31 0.04 -0.02 0.24 \n", - "MntFishProducts 0.26 0.00 -0.02 0.11 \n", - "MntSweetProducts 0.24 0.01 -0.02 0.12 \n", - "MntGoldProds 0.17 0.05 -0.03 0.14 \n", - "NumDealsPurchases -0.12 -0.04 0.00 0.00 \n", - "NumWebPurchases 0.16 0.03 -0.02 0.15 \n", - "NumCatalogPurchases 0.31 0.10 -0.02 0.22 \n", - "NumStorePurchases 0.18 0.09 -0.02 0.04 \n", - "NumWebVisitsMonth -0.19 -0.01 0.02 -0.00 \n", - "AcceptedCmp3 0.10 0.07 0.01 0.25 \n", - "AcceptedCmp4 0.25 0.29 -0.03 0.18 \n", - "AcceptedCmp5 0.40 0.22 -0.01 0.33 \n", - "AcceptedCmp1 1.00 0.18 -0.03 0.30 \n", - "AcceptedCmp2 0.18 1.00 -0.01 0.17 \n", - "Complain -0.03 -0.01 1.00 -0.00 \n", - "Response 0.30 0.17 -0.00 1.00 \n", - "Ec_Married 0.03 -0.04 -0.00 -0.08 \n", - "Ec_Single 0.00 -0.01 0.02 0.11 \n", - "Ec_Together -0.02 0.04 -0.00 -0.07 \n", - "Ec_Widow 0.00 -0.00 -0.02 0.05 \n", - "\n", - " Ec_Married Ec_Single Ec_Together Ec_Widow \n", - "Income -0.01 -0.02 0.00 0.04 \n", - "Education 0.01 -0.04 -0.00 0.04 \n", - "Year_Birth 0.05 0.13 -0.05 -0.17 \n", - "Kidhome 0.02 0.02 0.01 -0.07 \n", - "Teenhome 0.01 -0.10 0.03 0.05 \n", - "Recency -0.02 0.01 0.02 -0.00 \n", - "MntWines -0.01 -0.02 0.00 0.04 \n", - "MntFruits -0.01 0.01 -0.01 0.03 \n", - "MntMeatProducts -0.02 0.03 0.00 0.02 \n", - "MntFishProducts -0.03 0.01 0.02 0.05 \n", - "MntSweetProducts -0.01 0.00 -0.01 0.05 \n", - "MntGoldProds -0.02 0.00 -0.01 0.05 \n", - "NumDealsPurchases 0.03 -0.05 0.00 0.00 \n", - "NumWebPurchases 0.00 -0.04 -0.00 0.04 \n", - "NumCatalogPurchases -0.01 -0.01 0.00 0.04 \n", - "NumStorePurchases 0.01 -0.02 -0.01 0.04 \n", - "NumWebVisitsMonth 0.02 -0.01 -0.01 -0.03 \n", - "AcceptedCmp3 0.00 0.01 -0.02 -0.01 \n", - "AcceptedCmp4 -0.01 -0.01 -0.00 0.04 \n", - "AcceptedCmp5 0.01 -0.01 0.01 0.02 \n", - "AcceptedCmp1 0.03 0.00 -0.02 0.00 \n", - "AcceptedCmp2 -0.04 -0.01 0.04 -0.00 \n", - "Complain -0.00 0.02 -0.00 -0.02 \n", - "Response -0.08 0.11 -0.07 0.05 \n", - "Ec_Married 1.00 -0.42 -0.47 -0.15 \n", - "Ec_Single -0.42 1.00 -0.31 -0.10 \n", - "Ec_Together -0.47 -0.31 1.00 -0.11 \n", - "Ec_Widow -0.15 -0.10 -0.11 1.00 \n", - "\n", - "[28 rows x 28 columns]" - ], - "text/html": [ - "\n", - "
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IncomeEducationYear_BirthKidhomeTeenhomeRecencyMntWinesMntFruitsMntMeatProductsMntFishProducts...AcceptedCmp4AcceptedCmp5AcceptedCmp1AcceptedCmp2ComplainResponseEc_MarriedEc_SingleEc_TogetherEc_Widow
Income1.000.13-0.20-0.510.040.010.690.510.690.52...0.220.400.330.10-0.030.16-0.01-0.020.000.04
Education0.131.00-0.14-0.040.10-0.020.16-0.080.01-0.09...0.040.02-0.010.02-0.030.080.01-0.04-0.000.04
Year_Birth-0.20-0.141.000.23-0.36-0.02-0.16-0.01-0.03-0.04...-0.060.02-0.01-0.01-0.000.020.050.13-0.05-0.17
Kidhome-0.51-0.040.231.00-0.040.01-0.50-0.37-0.44-0.39...-0.16-0.21-0.17-0.080.04-0.080.020.020.01-0.07
Teenhome0.040.10-0.36-0.041.000.020.01-0.18-0.26-0.20...0.04-0.19-0.14-0.020.00-0.160.01-0.100.030.05
Recency0.01-0.02-0.020.010.021.000.02-0.010.02-0.00...0.02-0.00-0.02-0.000.01-0.20-0.020.010.02-0.00
MntWines0.690.16-0.16-0.500.010.021.000.390.560.40...0.370.470.350.21-0.040.25-0.01-0.020.000.04
MntFruits0.51-0.08-0.01-0.37-0.18-0.010.391.000.540.59...0.010.220.19-0.01-0.010.13-0.010.01-0.010.03
MntMeatProducts0.690.01-0.03-0.44-0.260.020.560.541.000.57...0.100.370.310.04-0.020.24-0.020.030.000.02
MntFishProducts0.52-0.09-0.04-0.39-0.20-0.000.400.590.571.00...0.020.200.260.00-0.020.11-0.030.010.020.05
MntSweetProducts0.52-0.08-0.02-0.37-0.160.020.390.570.520.58...0.030.260.240.01-0.020.12-0.010.00-0.010.05
MntGoldProds0.39-0.11-0.06-0.35-0.020.020.390.390.350.42...0.020.180.170.05-0.030.14-0.020.00-0.010.05
NumDealsPurchases-0.110.03-0.070.220.390.000.01-0.13-0.12-0.14...0.02-0.18-0.12-0.040.000.000.03-0.050.000.00
NumWebPurchases0.460.07-0.15-0.360.16-0.010.540.300.290.29...0.160.140.160.03-0.020.150.00-0.04-0.000.04
NumCatalogPurchases0.700.06-0.12-0.50-0.110.020.640.490.720.53...0.140.320.310.10-0.020.22-0.01-0.010.000.04
NumStorePurchases0.630.07-0.14-0.500.05-0.000.640.460.480.46...0.180.210.180.09-0.020.040.01-0.02-0.010.04
NumWebVisitsMonth-0.65-0.040.120.450.13-0.02-0.32-0.42-0.54-0.45...-0.03-0.28-0.19-0.010.02-0.000.02-0.01-0.01-0.03
AcceptedCmp3-0.010.000.060.01-0.05-0.030.070.020.020.00...-0.080.080.100.070.010.250.000.01-0.02-0.01
AcceptedCmp40.220.04-0.06-0.160.040.020.370.010.100.02...1.000.310.250.29-0.030.18-0.01-0.01-0.000.04
AcceptedCmp50.400.020.02-0.21-0.19-0.000.470.220.370.20...0.311.000.400.22-0.010.330.01-0.010.010.02
AcceptedCmp10.33-0.01-0.01-0.17-0.14-0.020.350.190.310.26...0.250.401.000.18-0.030.300.030.00-0.020.00
AcceptedCmp20.100.02-0.01-0.08-0.02-0.000.21-0.010.040.00...0.290.220.181.00-0.010.17-0.04-0.010.04-0.00
Complain-0.03-0.03-0.000.040.000.01-0.04-0.01-0.02-0.02...-0.03-0.01-0.03-0.011.00-0.00-0.000.02-0.00-0.02
Response0.160.080.02-0.08-0.16-0.200.250.130.240.11...0.180.330.300.17-0.001.00-0.080.11-0.070.05
Ec_Married-0.010.010.050.020.01-0.02-0.01-0.01-0.02-0.03...-0.010.010.03-0.04-0.00-0.081.00-0.42-0.47-0.15
Ec_Single-0.02-0.040.130.02-0.100.01-0.020.010.030.01...-0.01-0.010.00-0.010.020.11-0.421.00-0.31-0.10
Ec_Together0.00-0.00-0.050.010.030.020.00-0.010.000.02...-0.000.01-0.020.04-0.00-0.07-0.47-0.311.00-0.11
Ec_Widow0.040.04-0.17-0.070.05-0.000.040.030.020.05...0.040.020.00-0.00-0.020.05-0.15-0.10-0.111.00
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28 rows × 28 columns

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\n", - " " - ] - }, - "metadata": {}, - "execution_count": 97 - } - ] - }, - { - "cell_type": "markdown", - "source": [ - "Link de material explicativo sobre essa metodologia: [Categorical Encoding](https://pbpython.com/categorical-encoding.html)" - ], - "metadata": { - "id": "_YA6pE2GPCjg" - } - }, - { - "cell_type": "code", - "source": [ - "## Bom, fato é que em ambos os casos não obtivemos resultados muito expressivos sobre uma alta correlação entre o EC, a ED e a renda.\n", - "## Mas dadas as opções, prefiro trabalhar com as variáveis dummies da segunda tabela. Nela, temos a educação \"basic\" com uma correlação mais forte do que a var. \"education\" da tabela 3.\n", - "## Assim, vamos seguir a análise.\n", - "df_2[['Income', 'Ec_Married', 'Ec_Single',\n", - " 'Ec_Together', 'Ec_Widow',\t'Ed_Basic',\t'Ed_Graduation',\t\"Ed_Master\",\t'Ed_PhD', \n", - " 'Year_Birth', 'Kidhome','Teenhome', 'Recency', 'MntWines', 'MntFruits',\n", - " 'MntMeatProducts', 'MntFishProducts', 'MntSweetProducts',\n", - " 'MntGoldProds', 'NumDealsPurchases', 'NumWebPurchases',\n", - " 'NumCatalogPurchases', 'NumStorePurchases', 'NumWebVisitsMonth',\n", - " 'AcceptedCmp3', 'AcceptedCmp4', 'AcceptedCmp5', 'AcceptedCmp1',\n", - " 'AcceptedCmp2', 'Complain', 'Response']].corr(method='pearson').round(2)" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 1000 - }, - "id": "Utg6lDJ8pBT6", - "outputId": "fe99304e-d1b2-4702-a43a-f1ce6c26ec7d" - }, - "execution_count": 98, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - " Income Ec_Married Ec_Single Ec_Together Ec_Widow \\\n", - "Income 1.00 -0.01 -0.02 0.00 0.04 \n", - "Ec_Married -0.01 1.00 -0.42 -0.47 -0.15 \n", - "Ec_Single -0.02 -0.42 1.00 -0.31 -0.10 \n", - "Ec_Together 0.00 -0.47 -0.31 1.00 -0.11 \n", - "Ec_Widow 0.04 -0.15 -0.10 -0.11 1.00 \n", - "Ed_Basic -0.23 -0.01 0.05 -0.00 -0.01 \n", - "Ed_Graduation 0.01 -0.00 0.02 -0.01 -0.02 \n", - "Ed_Master -0.03 -0.00 -0.03 0.03 -0.02 \n", - "Ed_PhD 0.10 0.01 -0.01 -0.02 0.04 \n", - "Year_Birth -0.20 0.05 0.13 -0.05 -0.17 \n", - "Kidhome -0.51 0.02 0.02 0.01 -0.07 \n", - "Teenhome 0.04 0.01 -0.10 0.03 0.05 \n", - "Recency 0.01 -0.02 0.01 0.02 -0.00 \n", - "MntWines 0.69 -0.01 -0.02 0.00 0.04 \n", - "MntFruits 0.51 -0.01 0.01 -0.01 0.03 \n", - "MntMeatProducts 0.69 -0.02 0.03 0.00 0.02 \n", - "MntFishProducts 0.52 -0.03 0.01 0.02 0.05 \n", - "MntSweetProducts 0.52 -0.01 0.00 -0.01 0.05 \n", - "MntGoldProds 0.39 -0.02 0.00 -0.01 0.05 \n", - "NumDealsPurchases -0.11 0.03 -0.05 0.00 0.00 \n", - "NumWebPurchases 0.46 0.00 -0.04 -0.00 0.04 \n", - "NumCatalogPurchases 0.70 -0.01 -0.01 0.00 0.04 \n", - "NumStorePurchases 0.63 0.01 -0.02 -0.01 0.04 \n", - "NumWebVisitsMonth -0.65 0.02 -0.01 -0.01 -0.03 \n", - "AcceptedCmp3 -0.01 0.00 0.01 -0.02 -0.01 \n", - "AcceptedCmp4 0.22 -0.01 -0.01 -0.00 0.04 \n", - "AcceptedCmp5 0.40 0.01 -0.01 0.01 0.02 \n", - "AcceptedCmp1 0.33 0.03 0.00 -0.02 0.00 \n", - "AcceptedCmp2 0.10 -0.04 -0.01 0.04 -0.00 \n", - "Complain -0.03 -0.00 0.02 -0.00 -0.02 \n", - "Response 0.16 -0.08 0.11 -0.07 0.05 \n", - "\n", - " Ed_Basic Ed_Graduation Ed_Master Ed_PhD Year_Birth \\\n", - "Income -0.23 0.01 -0.03 0.10 -0.20 \n", - "Ec_Married -0.01 -0.00 -0.00 0.01 0.05 \n", - "Ec_Single 0.05 0.02 -0.03 -0.01 0.13 \n", - "Ec_Together -0.00 -0.01 0.03 -0.02 -0.05 \n", - "Ec_Widow -0.01 -0.02 -0.02 0.04 -0.17 \n", - "Ed_Basic 1.00 -0.16 -0.09 -0.08 0.12 \n", - "Ed_Graduation -0.16 1.00 -0.59 -0.53 0.06 \n", - "Ed_Master -0.09 -0.59 1.00 -0.31 0.00 \n", - "Ed_PhD -0.08 -0.53 -0.31 1.00 -0.12 \n", - "Year_Birth 0.12 0.06 0.00 -0.12 1.00 \n", - "Kidhome 0.05 0.00 0.02 -0.04 0.23 \n", - "Teenhome -0.12 -0.02 -0.02 0.09 -0.36 \n", - "Recency -0.00 0.03 -0.03 -0.01 -0.02 \n", - "MntWines -0.14 -0.06 -0.03 0.16 -0.16 \n", - "MntFruits -0.06 0.11 -0.03 -0.08 -0.01 \n", - "MntMeatProducts -0.11 0.06 -0.03 0.01 -0.03 \n", - "MntFishProducts -0.06 0.10 -0.00 -0.10 -0.04 \n", - "MntSweetProducts -0.06 0.10 -0.02 -0.09 -0.02 \n", - "MntGoldProds -0.06 0.13 -0.02 -0.12 -0.06 \n", - "NumDealsPurchases -0.04 -0.01 0.01 0.01 -0.07 \n", - "NumWebPurchases -0.12 0.01 -0.03 0.07 -0.15 \n", - "NumCatalogPurchases -0.12 0.02 -0.04 0.06 -0.12 \n", - "NumStorePurchases -0.14 0.01 -0.01 0.05 -0.14 \n", - "NumWebVisitsMonth 0.10 -0.01 -0.01 -0.01 0.12 \n", - "AcceptedCmp3 0.02 -0.01 -0.01 0.01 0.06 \n", - "AcceptedCmp4 -0.04 -0.01 -0.01 0.04 -0.06 \n", - "AcceptedCmp5 -0.04 0.01 -0.01 0.02 0.02 \n", - "AcceptedCmp1 -0.04 0.03 -0.02 -0.00 -0.01 \n", - "AcceptedCmp2 -0.02 0.01 -0.03 0.03 -0.01 \n", - "Complain -0.02 0.03 0.01 -0.04 -0.00 \n", - "Response -0.05 -0.04 -0.02 0.08 0.02 \n", - "\n", - " ... NumCatalogPurchases NumStorePurchases \\\n", - "Income ... 0.70 0.63 \n", - "Ec_Married ... -0.01 0.01 \n", - "Ec_Single ... -0.01 -0.02 \n", - "Ec_Together ... 0.00 -0.01 \n", - "Ec_Widow ... 0.04 0.04 \n", - "Ed_Basic ... -0.12 -0.14 \n", - "Ed_Graduation ... 0.02 0.01 \n", - "Ed_Master ... -0.04 -0.01 \n", - "Ed_PhD ... 0.06 0.05 \n", - "Year_Birth ... -0.12 -0.14 \n", - "Kidhome ... -0.50 -0.50 \n", - "Teenhome ... -0.11 0.05 \n", - "Recency ... 0.02 -0.00 \n", - "MntWines ... 0.64 0.64 \n", - "MntFruits ... 0.49 0.46 \n", - "MntMeatProducts ... 0.72 0.48 \n", - "MntFishProducts ... 0.53 0.46 \n", - "MntSweetProducts ... 0.49 0.45 \n", - "MntGoldProds ... 0.44 0.38 \n", - "NumDealsPurchases ... -0.01 0.07 \n", - "NumWebPurchases ... 0.38 0.50 \n", - "NumCatalogPurchases ... 1.00 0.52 \n", - "NumStorePurchases ... 0.52 1.00 \n", - "NumWebVisitsMonth ... -0.52 -0.43 \n", - "AcceptedCmp3 ... 0.11 -0.06 \n", - "AcceptedCmp4 ... 0.14 0.18 \n", - "AcceptedCmp5 ... 0.32 0.21 \n", - "AcceptedCmp1 ... 0.31 0.18 \n", - "AcceptedCmp2 ... 0.10 0.09 \n", - "Complain ... -0.02 -0.02 \n", - "Response ... 0.22 0.04 \n", - "\n", - " NumWebVisitsMonth AcceptedCmp3 AcceptedCmp4 \\\n", - "Income -0.65 -0.01 0.22 \n", - "Ec_Married 0.02 0.00 -0.01 \n", - "Ec_Single -0.01 0.01 -0.01 \n", - "Ec_Together -0.01 -0.02 -0.00 \n", - "Ec_Widow -0.03 -0.01 0.04 \n", - "Ed_Basic 0.10 0.02 -0.04 \n", - "Ed_Graduation -0.01 -0.01 -0.01 \n", - "Ed_Master -0.01 -0.01 -0.01 \n", - "Ed_PhD -0.01 0.01 0.04 \n", - "Year_Birth 0.12 0.06 -0.06 \n", - "Kidhome 0.45 0.01 -0.16 \n", - "Teenhome 0.13 -0.05 0.04 \n", - "Recency -0.02 -0.03 0.02 \n", - "MntWines -0.32 0.07 0.37 \n", - "MntFruits -0.42 0.02 0.01 \n", - "MntMeatProducts -0.54 0.02 0.10 \n", - "MntFishProducts -0.45 0.00 0.02 \n", - "MntSweetProducts -0.42 0.00 0.03 \n", - "MntGoldProds -0.25 0.13 0.02 \n", - "NumDealsPurchases 0.35 -0.02 0.02 \n", - "NumWebPurchases -0.06 0.05 0.16 \n", - "NumCatalogPurchases -0.52 0.11 0.14 \n", - "NumStorePurchases -0.43 -0.06 0.18 \n", - "NumWebVisitsMonth 1.00 0.06 -0.03 \n", - "AcceptedCmp3 0.06 1.00 -0.08 \n", - "AcceptedCmp4 -0.03 -0.08 1.00 \n", - "AcceptedCmp5 -0.28 0.08 0.31 \n", - "AcceptedCmp1 -0.19 0.10 0.25 \n", - "AcceptedCmp2 -0.01 0.07 0.29 \n", - "Complain 0.02 0.01 -0.03 \n", - "Response -0.00 0.25 0.18 \n", - "\n", - " AcceptedCmp5 AcceptedCmp1 AcceptedCmp2 Complain \\\n", - "Income 0.40 0.33 0.10 -0.03 \n", - "Ec_Married 0.01 0.03 -0.04 -0.00 \n", - "Ec_Single -0.01 0.00 -0.01 0.02 \n", - "Ec_Together 0.01 -0.02 0.04 -0.00 \n", - "Ec_Widow 0.02 0.00 -0.00 -0.02 \n", - "Ed_Basic -0.04 -0.04 -0.02 -0.02 \n", - "Ed_Graduation 0.01 0.03 0.01 0.03 \n", - "Ed_Master -0.01 -0.02 -0.03 0.01 \n", - "Ed_PhD 0.02 -0.00 0.03 -0.04 \n", - "Year_Birth 0.02 -0.01 -0.01 -0.00 \n", - "Kidhome -0.21 -0.17 -0.08 0.04 \n", - "Teenhome -0.19 -0.14 -0.02 0.00 \n", - "Recency -0.00 -0.02 -0.00 0.01 \n", - "MntWines 0.47 0.35 0.21 -0.04 \n", - "MntFruits 0.22 0.19 -0.01 -0.01 \n", - "MntMeatProducts 0.37 0.31 0.04 -0.02 \n", - "MntFishProducts 0.20 0.26 0.00 -0.02 \n", - "MntSweetProducts 0.26 0.24 0.01 -0.02 \n", - "MntGoldProds 0.18 0.17 0.05 -0.03 \n", - "NumDealsPurchases -0.18 -0.12 -0.04 0.00 \n", - "NumWebPurchases 0.14 0.16 0.03 -0.02 \n", - "NumCatalogPurchases 0.32 0.31 0.10 -0.02 \n", - "NumStorePurchases 0.21 0.18 0.09 -0.02 \n", - "NumWebVisitsMonth -0.28 -0.19 -0.01 0.02 \n", - "AcceptedCmp3 0.08 0.10 0.07 0.01 \n", - "AcceptedCmp4 0.31 0.25 0.29 -0.03 \n", - "AcceptedCmp5 1.00 0.40 0.22 -0.01 \n", - "AcceptedCmp1 0.40 1.00 0.18 -0.03 \n", - "AcceptedCmp2 0.22 0.18 1.00 -0.01 \n", - "Complain -0.01 -0.03 -0.01 1.00 \n", - "Response 0.33 0.30 0.17 -0.00 \n", - "\n", - " Response \n", - "Income 0.16 \n", - "Ec_Married -0.08 \n", - "Ec_Single 0.11 \n", - "Ec_Together -0.07 \n", - "Ec_Widow 0.05 \n", - "Ed_Basic -0.05 \n", - "Ed_Graduation -0.04 \n", - "Ed_Master -0.02 \n", - "Ed_PhD 0.08 \n", - "Year_Birth 0.02 \n", - "Kidhome -0.08 \n", - "Teenhome -0.16 \n", - "Recency -0.20 \n", - "MntWines 0.25 \n", - "MntFruits 0.13 \n", - "MntMeatProducts 0.24 \n", - "MntFishProducts 0.11 \n", - "MntSweetProducts 0.12 \n", - "MntGoldProds 0.14 \n", - "NumDealsPurchases 0.00 \n", - "NumWebPurchases 0.15 \n", - "NumCatalogPurchases 0.22 \n", - "NumStorePurchases 0.04 \n", - "NumWebVisitsMonth -0.00 \n", - "AcceptedCmp3 0.25 \n", - "AcceptedCmp4 0.18 \n", - "AcceptedCmp5 0.33 \n", - "AcceptedCmp1 0.30 \n", - "AcceptedCmp2 0.17 \n", - "Complain -0.00 \n", - "Response 1.00 \n", - "\n", - "[31 rows x 31 columns]" - ], - "text/html": [ - "\n", - "
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IncomeEc_MarriedEc_SingleEc_TogetherEc_WidowEd_BasicEd_GraduationEd_MasterEd_PhDYear_Birth...NumCatalogPurchasesNumStorePurchasesNumWebVisitsMonthAcceptedCmp3AcceptedCmp4AcceptedCmp5AcceptedCmp1AcceptedCmp2ComplainResponse
Income1.00-0.01-0.020.000.04-0.230.01-0.030.10-0.20...0.700.63-0.65-0.010.220.400.330.10-0.030.16
Ec_Married-0.011.00-0.42-0.47-0.15-0.01-0.00-0.000.010.05...-0.010.010.020.00-0.010.010.03-0.04-0.00-0.08
Ec_Single-0.02-0.421.00-0.31-0.100.050.02-0.03-0.010.13...-0.01-0.02-0.010.01-0.01-0.010.00-0.010.020.11
Ec_Together0.00-0.47-0.311.00-0.11-0.00-0.010.03-0.02-0.05...0.00-0.01-0.01-0.02-0.000.01-0.020.04-0.00-0.07
Ec_Widow0.04-0.15-0.10-0.111.00-0.01-0.02-0.020.04-0.17...0.040.04-0.03-0.010.040.020.00-0.00-0.020.05
Ed_Basic-0.23-0.010.05-0.00-0.011.00-0.16-0.09-0.080.12...-0.12-0.140.100.02-0.04-0.04-0.04-0.02-0.02-0.05
Ed_Graduation0.01-0.000.02-0.01-0.02-0.161.00-0.59-0.530.06...0.020.01-0.01-0.01-0.010.010.030.010.03-0.04
Ed_Master-0.03-0.00-0.030.03-0.02-0.09-0.591.00-0.310.00...-0.04-0.01-0.01-0.01-0.01-0.01-0.02-0.030.01-0.02
Ed_PhD0.100.01-0.01-0.020.04-0.08-0.53-0.311.00-0.12...0.060.05-0.010.010.040.02-0.000.03-0.040.08
Year_Birth-0.200.050.13-0.05-0.170.120.060.00-0.121.00...-0.12-0.140.120.06-0.060.02-0.01-0.01-0.000.02
Kidhome-0.510.020.020.01-0.070.050.000.02-0.040.23...-0.50-0.500.450.01-0.16-0.21-0.17-0.080.04-0.08
Teenhome0.040.01-0.100.030.05-0.12-0.02-0.020.09-0.36...-0.110.050.13-0.050.04-0.19-0.14-0.020.00-0.16
Recency0.01-0.020.010.02-0.00-0.000.03-0.03-0.01-0.02...0.02-0.00-0.02-0.030.02-0.00-0.02-0.000.01-0.20
MntWines0.69-0.01-0.020.000.04-0.14-0.06-0.030.16-0.16...0.640.64-0.320.070.370.470.350.21-0.040.25
MntFruits0.51-0.010.01-0.010.03-0.060.11-0.03-0.08-0.01...0.490.46-0.420.020.010.220.19-0.01-0.010.13
MntMeatProducts0.69-0.020.030.000.02-0.110.06-0.030.01-0.03...0.720.48-0.540.020.100.370.310.04-0.020.24
MntFishProducts0.52-0.030.010.020.05-0.060.10-0.00-0.10-0.04...0.530.46-0.450.000.020.200.260.00-0.020.11
MntSweetProducts0.52-0.010.00-0.010.05-0.060.10-0.02-0.09-0.02...0.490.45-0.420.000.030.260.240.01-0.020.12
MntGoldProds0.39-0.020.00-0.010.05-0.060.13-0.02-0.12-0.06...0.440.38-0.250.130.020.180.170.05-0.030.14
NumDealsPurchases-0.110.03-0.050.000.00-0.04-0.010.010.01-0.07...-0.010.070.35-0.020.02-0.18-0.12-0.040.000.00
NumWebPurchases0.460.00-0.04-0.000.04-0.120.01-0.030.07-0.15...0.380.50-0.060.050.160.140.160.03-0.020.15
NumCatalogPurchases0.70-0.01-0.010.000.04-0.120.02-0.040.06-0.12...1.000.52-0.520.110.140.320.310.10-0.020.22
NumStorePurchases0.630.01-0.02-0.010.04-0.140.01-0.010.05-0.14...0.521.00-0.43-0.060.180.210.180.09-0.020.04
NumWebVisitsMonth-0.650.02-0.01-0.01-0.030.10-0.01-0.01-0.010.12...-0.52-0.431.000.06-0.03-0.28-0.19-0.010.02-0.00
AcceptedCmp3-0.010.000.01-0.02-0.010.02-0.01-0.010.010.06...0.11-0.060.061.00-0.080.080.100.070.010.25
AcceptedCmp40.22-0.01-0.01-0.000.04-0.04-0.01-0.010.04-0.06...0.140.18-0.03-0.081.000.310.250.29-0.030.18
AcceptedCmp50.400.01-0.010.010.02-0.040.01-0.010.020.02...0.320.21-0.280.080.311.000.400.22-0.010.33
AcceptedCmp10.330.030.00-0.020.00-0.040.03-0.02-0.00-0.01...0.310.18-0.190.100.250.401.000.18-0.030.30
AcceptedCmp20.10-0.04-0.010.04-0.00-0.020.01-0.030.03-0.01...0.100.09-0.010.070.290.220.181.00-0.010.17
Complain-0.03-0.000.02-0.00-0.02-0.020.030.01-0.04-0.00...-0.02-0.020.020.01-0.03-0.01-0.03-0.011.00-0.00
Response0.16-0.080.11-0.070.05-0.05-0.04-0.020.080.02...0.220.04-0.000.250.180.330.300.17-0.001.00
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\n", - " " - ] - }, - "metadata": {}, - "execution_count": 98 - } - ] - }, - { - "cell_type": "code", - "source": [ - "## Analisando essa tabela, vou manter apenas as seguintes variáveis na tabela de correlação e vou gerar uma visualização dessa tabela para facilitar a escolha das variáveis do modelo:\n", - "cm_1=df_2[['Income', 'Kidhome',\n", - " 'MntWines', 'MntFruits',\n", - " 'MntMeatProducts', 'MntFishProducts', 'MntSweetProducts',\n", - " 'NumCatalogPurchases', 'NumStorePurchases', 'NumWebVisitsMonth'\n", - " ]].corr(method='pearson').round(2)" - ], - "metadata": { - "id": "aWwt_iqvgBK9" - }, - "execution_count": 99, - "outputs": [] - }, - { - "cell_type": "code", - "source": [ - "## Criando quadro para plotar figura da correlação\n", - "\n", - "fig_1, ax_1= plt.subplots(figsize=(13,7))\n", - "\n", - "## Crianndo o título\n", - "plt.title(\"Heatmap das correlações\",fontsize=18)\n", - "ttl = ax_1.title\n", - "ttl.set_position([0.5,1.05])\n", - "\n", - "## Criando a visualização do heatmap com o Seaborn\n", - "sns.heatmap(cm_1, vmin=-1.0,vmax=1.0,annot=True,cmap='RdYlGn',linewidths=0.30,ax=ax_1)\n", - "plt.show()" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 570 - }, - "id": "T5QCyZJEnAf1", - "outputId": "e90ba8e8-36c4-4559-8d07-8ec0d5abad6a" - }, - "execution_count": 100, - "outputs": [ - { - "output_type": "display_data", - "data": { - "text/plain": [ - "
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\n" - }, - "metadata": { - "needs_background": "light" - } - } - ] - }, - { - "cell_type": "markdown", - "source": [ - "Analisando a figura, escolhi as seguintes variáveis explicativas pro modelo:\n", - "\n", - "- 'Kidhome',\n", - "- 'MntWines',\n", - "- 'MntFruits',\n", - "- 'MntFishProducts', \n", - "- 'MntSweetProducts',\n", - "- 'NumCatalogPurchases',\n", - "- 'NumStorePurchases',\n", - "- 'NumWebVisitsMonth'.\n", - "\n", - "Todas elas tem correlação maior do que 0,5 com a renda e nenhuma delas tem correlação maior do que 0,7 com outra das variáveis explicativas do modelo.\n", - "\n" - ], - "metadata": { - "id": "SGH8xLDrxRNh" - } - }, - { - "cell_type": "markdown", - "source": [ - "###### **Modelo de regressão linear:**" - ], - "metadata": { - "id": "r8vS1uKk9nwU" - } - }, - { - "cell_type": "code", - "source": [ - "## Criando dataframes do nosso Y e dos nossos Xs\n", - "\n", - "df_4 = df_2[~df_2['Income'].isnull()] ##esse comando foi preciso porque a classe lr não funciona com dados nulos, logo tive que fazer uma copia da DF que vamos utilizar, porém sem os nulos.\n", - "X = df_4[['Kidhome',\n", - "'MntWines',\n", - "'MntFruits',\n", - "'MntFishProducts', \n", - "'MntSweetProducts',\n", - "'NumCatalogPurchases',\n", - "'NumStorePurchases',\n", - "'NumWebVisitsMonth']]\n", - "Y = df_4[['Income']]" - ], - "metadata": { - "id": "SqhvI12Z8Lfq" - }, - "execution_count": 101, - "outputs": [] - }, - { - "cell_type": "code", - "source": [ - "from sklearn.linear_model import LinearRegression" - ], - "metadata": { - "id": "Ktt7RD_f1ym3" - }, - "execution_count": 102, - "outputs": [] - }, - { - "cell_type": "code", - "source": [ - "## Criando objeto da classe Linear Regression\n", - "lr = LinearRegression()" - ], - "metadata": { - "id": "zZcbdvbu8C1w" - }, - "execution_count": 103, - "outputs": [] - }, - { - "cell_type": "code", - "source": [ - "from sklearn.model_selection import train_test_split" - ], - "metadata": { - "id": "r7bd4TuAAV1b" - }, - "execution_count": 104, - "outputs": [] - }, - { - "cell_type": "code", - "source": [ - "X_train, X_valid, Y_train, Y_valid = train_test_split(X, Y, random_state=42, test_size=0.3) \n", - "## Vou separar para a validação/teste 30% da amostra e para treino 70%, esse método é chamado de holdout e é bem aceito no meio corporativo. \n", - "## 70% da amostra irá passar pelo FIT e os outros 30% serão utilizados para validação.\n", - "## o random state irá fixar ou não um valor para o train, teste e split começar. 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\n", - " " - ] - }, - "metadata": {}, - "execution_count": 112 - } - ] - }, - { - "cell_type": "code", - "source": [ - "from sklearn.metrics import r2_score, mean_absolute_error, mean_squared_error" - ], - "metadata": { - "id": "XBsoHbpRJYfV" - }, - "execution_count": 113, - "outputs": [] - }, - { - "cell_type": "code", - "source": [ - "r2= r2_score(Y_valid, YHat)\n", - "print('As variáveis explicativas do meu modelo explicam', (r2*100).round(2), \"% das variações na renda dos clientes\")" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "VcQDedCmJUtC", - "outputId": "925b1241-6945-4810-d412-2d1da933149d" - }, - "execution_count": 114, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "As variáveis explicativas do meu modelo explicam 65.08 % das variações na renda dos clientes\n" - ] - } - ] - }, - { - "cell_type": "code", - "source": [ - "m_abe=mean_absolute_error(Y_valid,YHat)\n", - "print('O erro médio absoluto do modelo é:', (m_abe).round(2))" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "MtbOJ6PEM-HX", - "outputId": "c2ff6901-5347-4619-c24d-f6c9855415cf" - }, - "execution_count": 115, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "O erro médio absoluto do modelo é: 8683.5\n" - ] - } - ] - }, - { - "cell_type": "code", - "source": [ - "m_sqe=mean_squared_error(Y_valid,YHat)\n", - "print('O erro médio quadrático do modelo é:', (m_sqe).round(2))" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "kvpHmglINPQS", - "outputId": "58e71a56-df51-4a62-9aa0-3be9c4b44a93" - }, - "execution_count": 116, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "O erro médio quadrático do modelo é: 161400019.55\n" - ] - } - ] - }, - { - "cell_type": "code", - "source": [ - "import math\n", - "m_sqe_sqrt=math.sqrt(m_sqe)\n", - "print('A raiz quadrada do erro médio quadrático é:', (m_sqe_sqrt))" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "YG8Vrlkt5lFu", - "outputId": "bf46593f-32dd-46e8-a401-0a2da0a63226" - }, - "execution_count": 117, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "A raiz quadrada do erro médio quadrático é: 12704.330739940186\n" - ] - } - ] - }, - { - "cell_type": "code", - "source": [ - "## Vendo se os valores previstos ficaram bem ajustados\n", - "sns.distplot(YHat,hist=False,label='ValorEst')\n", - "sns.distplot(Y_valid,hist=False,color='r',label='ValorReal')\n", - "plt.show()" - ], - "metadata": { - "id": "JytAQ_6p5lkz", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 365 - }, - "outputId": "e81baf55-c102-45fc-a180-b1737136f205" - }, - "execution_count": 118, - "outputs": [ - { - "output_type": "stream", - "name": "stderr", - "text": [ - "/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `kdeplot` (an axes-level function for kernel density plots).\n", - " warnings.warn(msg, FutureWarning)\n", - "/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `kdeplot` (an axes-level function for kernel density plots).\n", - " warnings.warn(msg, FutureWarning)\n" - ] - }, - { - "output_type": "display_data", - "data": { - "text/plain": [ - "
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\n" - }, - "metadata": { - "needs_background": "light" - } - } - ] - }, - { - "cell_type": "code", - "source": [ - "## O resultado talvez pode ser melhor, vou aplicar um modelo polinomial para ver se será mais proveitoso." - ], - "metadata": { - "id": "y70w3qkTm-M5" - }, - "execution_count": 119, - "outputs": [] - }, - { - "cell_type": "markdown", - "source": [ - "###### **Modelo de regressão linear com polinomial transformado e usando um pipeline manual:**" - ], - "metadata": { - "id": "_4WwzbFeFtR2" - } - }, - { - "cell_type": "code", - "source": [ - "from sklearn.preprocessing import PolynomialFeatures" - ], - "metadata": { - "id": "6gdKOn1OFylj" - }, - "execution_count": 120, - "outputs": [] - }, - { - "cell_type": "code", - "source": [ - "## Criando as funções para o pipeline:\n", - "\n", - "def aplica_polinomial(X_train3, X_valid3,grau=2):\n", - " pf=PolynomialFeatures(degree=grau)\n", - " X_train3_pf=pf.fit_transform(X_train3)\n", - " X_valid3_pf=pf.transform(X_valid3)\n", - " return X_train3_pf, X_valid3_pf\n", - "\n", - "def aplica_modelo(X_train3, Y_train3, X_valid3):\n", - " lr=LinearRegression()\n", - " lr.fit(X_train3,Y_train3)\n", - " Yhat_valid3=lr.predict(X_valid3)\n", - " Yhat_train3=lr.predict(X_train3)\n", - " ##print('Acurácia do modelo de treino:', lr.score(X_train3, Y_train3).round(2))\n", - " return Yhat_valid3, Yhat_train3\n", - "\n", - "def evaluate_model(Yhat_valid3,Y_valid3,X_valid3):\n", - " r2_pf3= r2_score(Y_valid3,Yhat_valid3)\n", - " print('As variáveis explicativas do meu modelo explicam', (r2_pf3*100).round(2), \"% das variações na renda dos clientes.\")\n", - " m_abe_pf3=mean_absolute_error(Y_valid3,Yhat_valid3)\n", - " print('O erro médio absoluto do modelo é:', (m_abe_pf3).round(2))\n", - " m_sqe_pf3=mean_squared_error(Y_valid3,Yhat_valid3)\n", - " print('O erro médio quadrático do modelo é:', (m_sqe_pf3).round(2))\n", - " m_sqe_sqrt_pf3=math.sqrt(m_sqe)\n", - " print('A raiz quadrada do erro médio quadrático é:', (m_sqe_sqrt_pf3))\n", - " print('Acurácia:', lr.score(X_valid3, Y_valid3).round(2))\n", - " print('\\nVeja o comportamento dos resíduos:')\n", - " sns.residplot(x= Y_valid3,y= Yhat_valid3)\n", - " plt.title('Resíduos')\n", - " plt.show()\n", - "\n", - "def Pipeline_Regressao(X, Y, grau=2):\n", - " X_train3, X_valid3, Y_train3, Y_valid3= train_test_split(X3,Y3,test_size=0.3,random_state=42)\n", - " X_train3_pf, X_valid3_pf = aplica_polinomial(X_train3, X_valid3, grau)\n", - " Yhat_valid3, Yhat_train3 = aplica_modelo(X_train3_pf, Y_train3, X_valid3_pf)\n", - " print('Resultados do Polinomial de Grau:', grau)\n", - " print('\\nResultado do conjunto de treino ','- Grau',grau,':')\n", - " evaluate_model(Yhat_train3, Y_train3, X_train3)\n", - " print('\\nResultado do conjunto de teste ','- Grau',grau,':')\n", - " evaluate_model(Yhat_valid3, Y_valid3, X_valid3)\n", - " print('---------------------------\\n')\n", - " \n" - ], - "metadata": { - "id": "WnkdjQOrR6W1" - }, - "execution_count": 121, - "outputs": [] - }, - { - "cell_type": "code", - "source": [ - "X3 = df_4[['Kidhome',\n", - "'MntWines',\n", - "'MntFruits',\n", - "'MntFishProducts', \n", - "'MntSweetProducts',\n", - "'NumCatalogPurchases',\n", - "'NumStorePurchases',\n", - "'NumWebVisitsMonth']]\n", - "Y3 = df_4[['Income']]" - ], - "metadata": { - "id": "HYu_l07099lc" - }, - "execution_count": 122, - "outputs": [] - }, - { - "cell_type": "code", - "source": [ - "for i in range(1, 10):\n", - " Pipeline_Regressao(X3, Y3, i)" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 1000 - }, - "id": "L7it4IdOX9dL", - "outputId": "bb960074-cc30-4f37-a95f-95907e2a31d6" - }, - "execution_count": 123, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Resultados do Polinomial de Grau: 1\n", - "\n", - "Resultado do conjunto de treino - Grau 1 :\n", - "As variáveis explicativas do meu modelo explicam 74.66 % das variações na renda dos clientes.\n", - "O erro médio absoluto do modelo é: 7988.58\n", - "O erro médio quadrático do modelo é: 117803313.01\n", - "A raiz quadrada do erro médio quadrático é: 12704.330739940186\n", - "Acurácia: 0.75\n", - "\n", - "Veja o comportamento dos resíduos:\n" - ] - }, - { - "output_type": "display_data", - "data": { - "text/plain": [ - "
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\n" - }, - "metadata": { - "needs_background": "light" - } - }, - { - "output_type": "stream", - "name": "stdout", - "text": [ - "\n", - "Resultado do conjunto de teste - Grau 1 :\n", - "As variáveis explicativas do meu modelo explicam 65.08 % das variações na renda dos clientes.\n", - "O erro médio absoluto do modelo é: 8683.5\n", - "O erro médio quadrático do modelo é: 161400023.16\n", - "A raiz quadrada do erro médio quadrático é: 12704.330739940186\n", - "Acurácia: 0.65\n", - "\n", - "Veja o comportamento dos resíduos:\n" - ] - }, - { - "output_type": "display_data", - "data": { - "text/plain": [ - "
" - ], - "image/png": 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5wL0VyPu8KTmLLVhddAVMbE84d+1SHtrbPa59obBxe3ldkuHuYRQXJMJQCVHqq+JFb8yVmP5lKm0emzes5WPbdjOczo5rs7BeU2ahVbyBW0TuBr7ofzaq6gEROQnYrqq/KiJb/Os7/PHPABtzP6q62advwS3Ruh34qaqu8+mXFh5XymJr4M41rBbebIbTWVrqq2zK8ykqdbOd2Ch8ZChF50Ca5roEy+uS+RtuTTxCJtT8v8HLR4boG80CEBFoqIrRUJ0o2phcrOG5fyTDstoEg+mAukQUEWEglS0aCIrlsXsoQ10yyhkrGsYdn/ueE3tNWS8pMx9KNXBXtOusiKwBXocrAaxQ1QN+10FghX99CrCv4G0dPm2y9I4i6cU+vxVoBTj11FNn/kXmgI3gnZ3tezq5ausTDIxmyYYhhwdSXLX1CT598WvHtScA9I9kiQgMjGZprq+iJhFjOJ1FRMgEIcPpLNkgZCgdEBWICgTASCbkT998StEb8MSur3XJGApkQiUq0N41BMApS6qKlhq37NhLJgg4MphlKD1WzZTKhjx9oG/c8cVmDrjxgWe5afvzBKGSjEXIBqGVTM28qliwEJE64LvAX6pqf2GzgqqqiMx5H11VvRm4GVzJYq4+ZyZPeDaLrTPZtZus5HDFt3YxnA4QcQ1tqSBkdDDNFd/aRU0iysqGqvxnpIOQiLjfOdXxKH0jGT656Wy27NjLrpd7iEWFFfXViEDXQIrRbMAt//MCr1m1pGTAyKVfevPDpIOQmkSM5w4NEISKAvt6Rjh1aQ3pbMCVdz5OQ3Wc1U01/HJ/L6OZkCA8+s+yd8SVbrbs2Fty3MVN258nVCUWEbKBcmQozbLaRMn3GFNpFQkWIhLHBYpvqur3fPIhETmpoBoq15q7H1hd8PZVPm0/riqqMH27T19V5PgFMdO2h4l10YcHU/QMZ+gbyXDpzQ9P6aZ5rJvs2v2io5ebtj9PNgxJRiMEoXtyvrijl6279jPsn8ZVXSkgZzgdEIRKPJoiCJTOwXR+n+AG3jVUx/OBOXfDf8sND7KkOs5gKssrvaOIuF5Rw+mg5L9n4b9L10CKlQ1JBkYzjGbD/OeFCvu6h90GcOpS95AwmHKBLqS4/tFsyVLmlh17yYYh8WgEQRABQldyspKpmS+zDha+Z9JXgKdV9V8Ldm0DLgOu97/vLkj/kIjciWvg7vMB5T7gn3O9poB3ANeqareI9IvIObjqrfcA/zbbfM/UxCqPXBVHuSe8wmqM5w71M5AKWFobZ1ltctxNE1hUDeGVDFylrt0NP9rD3sNDhKrEoxEChSODGZbVwS3/8wLN9UkE1+uhUK7s2lQT51B/iokP7e5Jf5iWbJJELDqukbguEaW9a5BUJgSBmAgREZIxyXeJLWwnePZQP4MF/2aHB1Ps7x0lFnE371zTX8QHBA2h1rdjuO62HJW/QqFSspS5r2eYZDRCOlBCDfOflQlLv8eYSqtEyeLNwJ8AT4rIz33a3+GCxF0icjnwEvBuv+8e4EKgHRgG3gfgg8Ingcf8cdepard/fQVwK1CN6wW1YD2hZtP2kLvJXnnn42TDkP6RLMlYlPqqeD7gADMKRnOh0j24Sl27PQf7CUJ3cw9CxT84c6AvBbhgkIgKqWD83VaBqliE5XVJDvWnin5mqNA1mGZJdWzcmIgjQ2mygas6QiGjSkSUlY01ZIOQXS/38GsfvZeRzPiywJHBDMlYlBX1VXT0DDOaHcuT4oJOxkeF0WzI3q5BltcliQmky1SMnrt2adH01U01DKeyDGcy+QCZ68VV6j3GVNqsg4Wq/g9jD3kTva3I8Qp8sMS5vgp8tUh6G3D2LLJZMVNteyj2RA74qqjA1T2Hyiu9o5y8BOqSMTp6XFfOxdIQPtNSVDHb93TSP5LhQN8IVbEozfVJ6qviHB5MkQ3H/oAmPn1HBPb3jrKsNsHhwRQT4gWj2ZCnDvSPK3VIwR1Vcec+qbE6H+xqE1FiUcn/G+SECgf7RsgEWrK6KBuEdA2kaK5PHpUXIB8owHXHTWVDN61I0bLRmNpEhK279hdtL9m8YS2bv7GTaATCcOw7LamJcc+TB47qInw8VFmaxcdGcE/TVGYPLTU1xPX3Pk08KiRjEVBX7ZFrXM0FnMU0KGu6A9FKDTrMXY+aRJSICOkgZH/PCIcHR+kZzpCICtGIHPXEIcCy2gQAfSMZElGKKlq9o2O35lCVwVQ2P+q6vXOQwwNpVxKIjP/U1CSBAlyJJx2EHOgdmeSofBYIQiVUHReUilnbXF9yRPjGdS3UJaNUxVyQq01EOXVpDQ1VcZ7rGrTV98y8sGAxTVNZQS/3RB6EyguHh3i5e5jO/lHau4aojkdZXpckRAlDBZThdEBHzwjPdQ7QO5ymbySzKKaynk7gmmzupNz1aK6v4uTGahLRCAoMpQLqq2Kc1FhFRFzAKNRcl2BlYzWnLKliNBviOw0dJRGNjAs0quOf4UOFF48M89yhAbJBSFbdALx0Nix7Ey/G9cbKzSkVOSrgFHIBY3zaxKNzb58sEJ+xooGVje76AbzSN8K+nhGiYvNImflhs87Ogbfc8CBRcXXuIq5aJJMNCdTdGBJR108+KKgmWdmYZFltctxgr6G0uzEXG408H1UNU5kBNRcMdr3c479HFfVVrhotN+gw11YxoTs1fSMZVvlqvcHRLF2DY43U8aiwbmVD/jzP+3EMxSpzquNRQg1JZZXqeOSodobCm3M0IvlurjMV99VXEYFELMpoJpjV+WoTUdY21zGczpKIRlhSkyjaffhvtj5B73AmH1zSgRvjsXppDfVVcQZGM3T2j5IKlDeuWWpVUmZGbNbZCio3x9PqphoO9afyvWRGM2G+fjtUV8+eaxcVXABJxsZ6zjRUx2mqTfLfV7+VzRvWsnXX/gWpaihXiiosTYTqqlte6R1lYDQDjD0pT1ZC2bxhLf0jGY4MpYlFhJj/iwwCpX8knS9Z5RS7KbuqHqhJRPn11U1EI0JNPEI8Mvae3M9sAwW4gLhuZT0ibqbamZyvMIDVJCIMp7P0j2ToGkzROTBKKpPlkReO8N5bH+PMj/6IX3T00lyXzLez5EpEocKB3hEGRjO80jtKJlSqYhGrkjIVZ8FimqYyVfXmDWvJhCFBGI4bGDZR7iYTjQgH+0bZ2zXInoP9HOh1VVJw9GynQah09o+y+Rs752QywomBEOCO1nP476vfyh2t5xStbqtJxHxV0FgbDIwPCKXaeTauayHppwd3XUOhMRklEYtwsD+VD1ATq6gKpQM32C0q8MLhQcJQGc6EZIpc+klOMy1Xn7+Oppp4yZ4d5eRKlACHhzIkohGW1SZorI4zMJKhazCTL2UNZwK+8GA7HT3DrGhIEo0I8UiEeERQXJXYKz3D5MLW8rqkVUmZirNgMU1Tmap647oWTm+um7Rffe5G4frfqytthEo04rpeDoxm2b6nc1wjc+7pMfcUX+mnx9yUGo+/3MPBvhEef7mHq7Y+UfL8hXnLtcOoKqlskH9S7hlK8Q93/5LaRJR4RI4qoWzf08mBgVS+hBUq9KWCfJDNBZSLXrOy9LUUOLkhSW0yRtdgetIn/WI9mKbr8GCKjeta+PTFr2XNspl3PMhlRRXauwbZ3zviBmwOuYGFIu7vJHdthjMhhwZSRBAiESHm20sESIeuevPkxmoafG86m07GVJItqzpNU12W85oLfo3Lb3+MhK82KDZgLMcPAHb18aGfBVVDNn9jJ8lYhKFUhnTWNYQjEAWS8Wi+K+v19z5dkYFzN/xoDz3DGaL+RqQKPcMZbvjRnqLnyw1sC0J1de3VcQZSWUQl34idCZUlfgR1JlA+uenso0onESAz4fqE6to1rtr6hOs2O5SmmJMakzRWxXnhyDDpbJi/rpN3VJ25WMS1FeQMFyu+zEAmUDJBwOHBowcXirhgEYRKJnBzWeXGWUREOGVpFQf7U6xsrDrhp5Mxc8dKFtOUq3/vH8nwSt8I2UDzT3+FT/m50kWkTL1H7sYguBura+fQfH14RKBnOMtoNsjfILIKqWzAnoP9dPSM8MyhynSf3Ht4iIi4G5DguvZGxKVPtH1PZ35gmwCZIKRnOENdMsaWP34DS2oSxKOueu2ZQwMc7BslEwRHVYvs6xkmEin+Z6i4YPVi9zDDqSxRfylj4gbjxaNC10CKI0PpOQsOOdXxKNVx165U6/vw5kqZlaraAvd98+fzXygWiRCqG4tzRksdEV8dif/72N87StSX2hZDLzpzfLJgMU25+vdDA6OuKsn/Z2Vj1VHVUddc8Gu01Fdx2vJamuviJc5YwPdMi/n6/0Q0Qsb3eFEdq5IA1x0zKkLKP01nA51R98kbH3iW1/zjfbzq7+4hlS0+0V0xW3bspaE6zqqmauK+FBGLuF5TW3bs5ZEXjnCgL0UqGxL1pavDA+l8W0zO6qYaN7fThDuu+O+Ue6JOB2MNyVl1gTXXTtJQHWdlY9X47rNT+hZTN5IJGM0EZAPl/W85DRirhmuuS+bzPFuZIMx3xXXXFBRXMn3/W07j6vPX0dJQxbLaBCEQqAvWjdUxBIpW9RlTCVYNNU25OZ42f2MnoSrJaCQ/GllVx9URF84Hlc6GxCIZP0320bcyxdWnB9mCag2JkA2VWFQIFU5ZUs1LR4bzx+cmsIvg6tFzjcupbHBUtVgxH7lzF9//uZtFPterNVBIZwMXANSVfF69/OjR6bte7iEIQ5IFo7H7R9J09I6SiI09gwSh5sdQhOLGNhTavGEtu17uyZeiCmOVMr7Kp1BUhIbqGN1DmfwTf3NdYtxEgnOhoTrGa1YtAaA+GaO9c5BAlXi0+L/rdFXFIpy0pJqu/lGyoZtdtzYe5f1vOY0rzzsDgOtwU8aEquNGww+nszTVJvnRR2x9FFN5FixmYOO6Fl5/atNR034cGUoxlAp4yw0Pjms7KJzWeudL3VOqMongDgpDJatQFXdzSBVOWpcT4mZfzc2equqeUC+/vY0zWuq4+vx1RWdQ/Y+fH8hvF54zUIioa2xfkoxzzQW/Nu59H9u229eju9HYLx0ZJhIRwlDHTQ/iv4JrrA58O0JsfOY3rmvhgxtf5WacLchEueuTCUK6hzKsXV7LSCagJhFjRWM1fSOZo+aQmg23PrebGiQ3FiJXausaTOXHW0y1RFbus1oaXLtDc0NVyYWxNq5roaE6zqlLa8aNXbEGbTOXLFjM0MQpxwtXZys16Z4bXHdkSucPcU+V4G7eQ+mAJ/f3lTxecTfQ3CR8sYirS3/h8FC+kXgwHeSD2JYde4vekHOBLCKCKvkqlomD7xqqYnQPZ/L9/XM3y0ygDIxm8oP4cvfQiA9ig+mA1/zjfYAbeJdbJS4XMFLZqTUY576vqtI/4sZ1ZIOwooEi970EJa3uppy7IW/ZsZfG6ji1iRiHB1NkgqDMmcYIbpGkg/2jbmCmQjIqnLSkOj+gsdyNf3VTDS8eGaR/JEs6CElEIzRUx1izrG5W39eYUixYzNDEldOGUgHNdQma690iPMUm3XtobzeJqJSsWpmt3MAz8N0t/UI53UNpjgyliQgc6huh7aVusiXykEvNTcU9kgn4izt2MZp1U2MH/km6dyRLWGT0vwIvdw/TXJfMr0GRazTPqiLq1m6IR4WRDDx9oM9V6YVK1M8RFY9IvoqtnPauoXyngJ6RbMUbul2PLkj6ealyPYz29QwTFTg8mCYdhNP6zPqqGP9zzdgcm7kld4NQ2ds1SDoIiYpw2vLakuc4d+1SHn2x219bV3rrHEhz6W/YLLRmblgD9yxsXNeSH7DWUB1nuX8Kz5n4dLivZ5jG6vmJz5lQGc24sRuB5rqiuhtfJph81HEENxX3YCpLNlAGUq7ba8wPAgvU3ZxK1byECt25sQL5NCUmY20jsUgEDZU+H3QyoZsrKwyVzCQDGQup4kYzB8rhoQzNdYk56xHlqvrGehjVJaLs7x11QXcaU+YIcNbJjePScqPYO3pG8qXDbKh0DaZK9mp7aG839ckoQaikskoQKvXJKPc8eWDS2QWMmSkLFhUylUn3VjfVMJAKJp14bi5NtUCTiEXyjeUH+0fz6VN92gdflRURVjQkOevkxvzYjVDHRlGH/rjcjEW+WJ4AACAASURBVLMi4tbDnmI+dcLvzoHia1oUEsh3wZ2OTKDjehjl2gpCtOhI8VIU2Nc9NO5mvnFdC8tqE8SiLhjHoxFOWVJNY3W8ZK+2Zw/1M5QOiEcirhtxJEL/aJZnO20WWjM3LFhUyFSmLj937dL80/5iFJOxtgURGEkHU25DKCTi6uQjuIWHnuscIMJYN8/c1B2h7w6sCsmY632VG5cyk3A6lcuaKxlNh+DyVzjdSddgCtAZ9YA6PJg+6mY+mA54dXMd61Y2sLa5jobq+KTtFrnPjUQEETeiW30hx2ahNXPhmAkWInK+iDwjIu0ics1C52eiqUy6t3XXfhLRo9dtWCyy6v4gsqpksuGk6zpMRvADxaLCqiVVbiW60E0F0lQTB4VsGOansVB1k/OdvKQqv8ZHLIKfWLAyVysqrhE5JyLuu07l9BGB0wqm9di+p5OB0ey4RZtyppLbwA+6zAZuypbN39iZnw2g0GQjsBNuAIafyFDzExoWfp/+kQwH+0Z59MXuSaukyk2MaQwcIw3cIhIFbgLeDnQAj4nINlV9amFzNl5hN9mJcqN9T2qsdiun+fr2xSabGyY+Q7mSAgIr6qtoqI7TUJ0YN/32c50DDKVcyStQCPxiQkvrErQ0VHHx60/hpu3PE/qSSCW4zxn7XuHEOqxJNNUm8t2Ht+/p5Mo7H8+3q0x8eyzCpNVSUXFzOOVmAFBVsqHr2dU/mvUz9kp+MOOm155c9Dynt9Qf1RtKo27UPZA/P7ggWWpJ3EovnWuOX8dKyeKNQLuq7lXVNHAnsGmyN7z00kv84Ac/ACCbzdLa2so999wDwOjoKK2trfz4xz8GYHBwkNbWVh588EEAent7aW1tZceOHQAcPnyY1tZWfvaznwFw8OBBWltbeeSRRwDo6OigtbWVnTt3AvDiiy/S2trKE088AUB7ezvt3/9XYn37aaiOsyLspr7tq8QH3TiHWN9+Gtu+RnTwkNvufdltDx122z0v0tj2NSLDbkny+JHn3fao60obP/wcjW1fQ1JudHSi6xm3nXbTdCQ6n3LbWdf+kDj4SxrbvgaBa4ROHnjCbYeuzSX5yuNu20vub6Nh12357ap9j9Lw+NfHtl9+iPqffwtwN8+al/+XJU9+m8ODKZ460M/z//tDuh68lb2Hh9i8YS3vjO6m9snv5ifBq3n+QeK/+B4H+1K84dRGInt+TP3T24jgBuXVPHsftXt+mP+82mfupfaZsWXYa/f8kJpn78tv1z29jZr2+8e2d3+fmucfHNv+5Xep3rs9v13/5Heofem/84Gp/ok7qX7pfwFXZbbqme9w4PGfjI0xeehr1Ox/LP/+hl23kdzv1k7JhNDY9jVO7ttNS10CCQMa275G8qD7W5AwQ+1jX6Xn2TbXuyw9SmPb14gfcs89w4MDxB66herDz7C0Ns53/vdp3v3H7zvqb++3l/YRj0ZpiQ6x/Oe3sTzl/rbqMj103nsj3fuec/kf6KSu7WvE+l8hHhVu/N4OWltbeeaZZwD4wtafktr+JeJDXYgIsd59pLZ/iX/b9hAAO3fupLW1lY6ODgAeeeQRWltbOXjwIAA/+9nPaG1t5fBh97e6Y4c7f29vLwAPPvggra2tDA4OAvDjH/+Y1tZWRkfd3+I999xDa2sr2axb2eoHP/gBra2t+Wv7/e9/nyuuuCK//Z3vfIcrr7wyv33HHXfwkY98JL/99a9/nauuuiq/feutt3Lttdfmt2+55RY++tGP5re//OUv84lPfCK//cUvfpFPfepT+e3Pf/7z3HDDDfntz372s3z2s5/Nb99www18/vOfz29/6lOf4otf/GJ++xOf+ARf/vKX89sf/ehHueWWW/Lb1157Lbfeemt++6qrruLrXx/7f+sjH/kId9xxR377yiuv5Dvf+U5++4orruD73/9+fru1tXXW971SjomSBXAKsK9guwN408SDRKQVaAVobGycuHtBVcUijGYDEkBdVYwwHqVxSTWdkahrLKBg7ehj2OqmaiLdSXp63frToV8/whVYlI9t2w3PHSYTuhkTC5/MFfjhkwdZ+uKLjGYrP2ZiMvXJGMuW1pAJAob9nFjxqLCsNsGBvlHaDw2wzZcORYqXCnPtMSIQhCErGqupjgv9EfdUlogIGkI6G5LyswwX+yePAC1Lqqmqq2Kwb5gDvoSwfU8nX7zncV7a18uhXxzg4g2/yY6fD/FyqCypTvCJi19L5ysd/NtTEXqCkHhEWFafJIiMjRE5dHiU5QWfdWhgNF8ayX++CAf7yy8ba04sx8RKeSJyMXC+qr7fb/8J8CZV/VCp9yzkSnnFlFp17uLXn8LWXfvdjKMDaTKLsGpqOtyCQ1L0eySjwqqlNXT0jBCq5hctyt2rCv8Up9MraraiEeHkxio+uelsrrzzcYbTAclYhOV1SRqq4+NW/Iv6iftKBYtIxPXoClX5lWW1+X/rvpEMAsSiQt9wZtxstRO/vwBnn9Lo09yKgp/cdHbZVQsL5cZuFI6mz32PwlHhUz3OnDiO9ZXy9gOrC7ZX+bRjRqkG8CvPO4PrLjqLNcvqaKyJ53sKCSxYF9vZyISUDHgpv/pdKhuSCcambc/14ikk81jMigocGUy7hubRLBGU5vokIrC3a5CXjgyx6+Ue6pMxDg2kiPouvhMpY/9DhQq9Q2l2H+jn+a4huofSBKGyvK6KV7XUj1sHY+L3V2DPwX72dg1yZCjFqqaaKa2jUmgqvfMKjzs8OMrznQM8daCPjp4Rzl1rg/vMeMdKySIGPAu8DRckHgP+UFV3l3rPYitZTFWuBJLOBvQMZ2bUddVMX1RcCWPi6Pq4770WiQh1yRjdfjr0UqUeV30VIRuGBCH5KcxTfh3dFfVJWhrcKP+nD/Tl1zKZOIliMuYmjwwVPvzWV3PXzo6S65j/99VvLZqX3BQtHT3D+RULi5VCbnzgWW7a/jxBqCRjEeqrYiRiUZu19gRVqmRxTLRZqGpWRD4E3Idb++erkwWKSsv9TzfbxYWmonAakZ6Xe+bkM8x4USG/2NPE/k258QxJ3I2/KhY9avBlodxI+dAHgZhfqyMibp6srsFUPlg01yc5PJgmKkLWvyE35iRQNwV7fVWMh/Z2s7qp5qjqonKLG03WO6/QQ3u7WdVUfVRVVOFUNcYcK9VQqOo9qnqGqr5KVT9V/h2VMZU1tystN41Ic31yautgTHDsVV4trEAhDMP82I9i0qFyqC9FNAIl1moC/BKvS6rcGtv+ZNmCqVFChf6RNMPpLPFolL/47VfzulObOKnRrQty6tJqTl9Rnx+ct7wuSUfP8JSrlWaicHncHJvB1kx0zASLhTLduuJKWt1UQ0N1wnXBnMb7Fn/F4uytqE9OaUBduUNy58iWmeJJ1U1Pkg2VJVWTFMjVrWKYW6gqG4RHteG81D1C33Ca2kSUu3a6Lqmf3HQ2py2r4dBAKt9e0T+SyZceyg36nI2pTFVjzDFRDbWQCtfczpmvp67cNOj11XFaGqryvWqa65LsOejGVEQjgqrOW8+hxaJrMEUiGpl0QkOYwrohfvnaqTbdjWTCSduRFOgcSFOdiDKcDo6amTcWcZ93eChDPBZhWW2SzoFR/mbrE2SCML86YCYI2d87Qk08QjwiR62RUkkTp9vP9bSyJVlNIStZlLGQT13FniY/c/FrufcvN3DO2mWsaEgSi8gJFygA3/hbfoS3AEtKzPQbjwjxaISonxNrqqW38r2blbXLa1laM/4hI+Y/L6d/JJsvrQ6msqQy4bhlaiMCI1lXMpnLKtC5LLWY44eVLMpY6KeuUo2UuXxBZl7ysRhNZV0QBU5qrOZP37ySh/Z288gLR1DNzTyr4x4EYhGhNh6hPxWULJHkAspkn1wVc8u8ntJUw3B6gNFsSFXcrRcOYxMopgumYg/83FmFMoG6AYCB8kLfkFvnIiJcf+/TFb+RT7Ux3Jy4rGRRxmJ96srly0/DhOCelBPRE+efdColgURUWFKT4MrzzuCO1nN402muRCZy9BxO2VDpmyRQuHmdhMkucVRcL6ecFQ3udS4YhH7UdsTPEZV/X8TN6/SKH/AX9WuHhAodvSNkAyUqbuna57oGbbI/M++sZDEFi/WpK7cW+AuHB91KeMhxMWXIVOVu6pOtjhco49qXNm9Yy4e//XjRyf6iEZl0Le1YxC192tEzQiLqPjc3JUkEt056Y00svzQquC65q5uq6Rxw63UnYxGqEzEGUgEN1TFUXemmLhmjbyQDKBHctCC57xWGSsQNjXcPBSLWrdXMOwsWx7hcddSy2gQDo1lS2dAFDD+NRql738RBYMeyyYJFGCoahlx688Ps6xmmPhljYCRb/BxlWrkDdU/8TTVxltUmGEoH4wa75bpZT6yy/OSmswHGDZA7d+1S7v3lQZ7rdBPsnbashtFMlkxW8w3osajkVzVUND/Se2Vj0rq1mnlnweIYN3Et8NyN6Kbtz+dvOrnCRjQiJKLCiJ/+OqxAy/iS6ji9IwvbbjLZGHcFDg2mSSbcOJn2zkFCjg4wuZnZC9ML2yfcJIERWuqr+OjvnFn0qb7Yv0Vh76WJU4Nv3bWfVU3V+cAymnHrnCeiEb+ueO7TXTVWIhqhuT5JNCK0+LXejZkvFiyOA6WqyT73k+fyq95FxNWJN9bEyQykKzYYIxOEJGORRTstieAaig/2jdJcn8yv1lfq6zdUx0hlAkazYzPiRgWW1ib49MWvLVv1M9Uqy8LxO+BWt4uIkA7dWuW5SBUVN9XIqUtrrFurWVAWLI5TV553BgA3bX+ebBiSiLpAEY9GeXVLHb0jaQ72pWYdM1LZgMUwZjzqq9Umfp/cdiob8ErvqGskFjcuJSZ+sSdvdVN1vsro+nuf5oUjrqpn7fJarj5/XUXbCIqN31H/PWIRyS9otLKhmqF0QEt9Vdk5noyZSxYsjmNXnncGr1m15KhqEYCPbdtNMjY24d1Mg4YrUCx840e5GrVsCLFISEQiiAiJiJvwD18ld+HZKzjYn+Yf7v4lq5tquOaCX5vTG3KxuZ6iESEqwtrmunzacDrL6S01Nl24WXAWLI5zpapFrgNu+NEenu0cJBkXVtQn/ToT07v1RyN+Wos5roWKR4qvkVFOYbtDoKBhyNKaxLgG6nPXLmXrrv3zurRosfE7dckYAjaS2ixKx8QU5TNxrE5RPt8Kp7HuG8lQk4gyMJolG+qU2iFyE9Cls8GUR5JHBZpq4hwemrxhPNe2kFs0KCJjXVtFxmaEnWhiT6+Y7xKrQENVjBsved24ILBQCwBNnEL83LVLuefJA3Na/WVMOcf0FOVm7hSWPHJdP0ezKWJ+xbdi9+OlNXG6hzN+VTxneqURoSYZZ1UsQudgOn/Tj4rr2ZRfFMmnNVbHGUoH1CejLK9LIiI87efGys21VBgcJj7/BKGbbykWjRwVKGDh5v8qdu3jUeH0ljpGMgFD6dJToRsz3yxYmLxc18/c0qJV8ShRgcF0kL8ZN1TF+NWVDTyxr9eto+1NtYCaiLqRyfGokEgkiESj45YKzU2U2DUwSjpQErEIp7fUH9Wo+6v/cC+qStTPFx6Emp8+IzIhyOVGQn9w46uKPqXPZK2ISivWO8rWlDCLiQULM87GdS3ceMnryq73fOMDz/KFB9vJhiERGStZNCSj9KdKPxGn/ayq2SAknQ355Kazx1XFlBrDUGj7nk4/yZ4iQUDMT5WRKwlFIuL+sP2MsolohOp4JN9DbKKFnv8LFnZ2Y2OmYlbBQkQ+DfwekAaeB96nqr1+37XA5UAAXKmq9/n084Ev4Fa8u0VVr/fppwF3AsuAncCfqGpaRJLA7cAbgCPAH6jqi7PJt5lcucFlMNY195b/eYGhdEBNwpVCmhuqSHUPk/Yjj4uNaQjVremwaknVtKdSyVXXNFbHSWVTqLo1vxNRaKpNsLw2QSbUou0Ps/m+c20xlG6MmcxsSxb3A9f6ZU9vAK4FrhaRM4FLgLOAk4EHRCT3WHcT8HagA3hMRLap6lPADcDnVPVOEfkyLtB8yf/uUdVXi8gl/rg/mGW+TRnlbuLb93Ty0N5uGqrjnHVyY/4pfMuOvfQNpxlIBSytjZOIRni5e2RcwMgFkEMDKbbv6ZzWTTlXXdNYXUUyFuXwYIpUNqQqHuUzF78WYEalhIWe/2sxlG6MmcyspihV1R+ram6inYeBVf71JuBOVU2p6gtAO/BG/9OuqntVNY0rSWwStwr9W4Gt/v23Ae8qONdt/vVW4G0iJ9J0eYtPqaVmAe5oPYe2j76DLX/8BtYsqyNUSMQixCKuB5PgfscjgirTXnGwcAnQhuo4a5vrWLeynsbqeP6Gv1CzBG/f08mlNz/MW254kEtvfnhaM8Mu1tmNjcmpZJvFnwLf9q9PwQWPnA6fBrBvQvqbcFVPvQWBp/D4U3Lv8SWYPn/84YkZEJFWoBXg1FNPneXXMaVMpTG28En90psf5tEXj5CMFazpECqxqEy7Tn4q1TULUUoo7M0007EaC126MWYyZUsWIvKAiPyyyM+mgmP+HsgC35zLzJajqjer6npVXd/c3LyQWTmuFT7d50zWGLt5w1pikci4NR1ClPqq2LTr5DdvWEsmUIbTWVTd78VQXVMYQAdTWQ72jbK/d5gr73zc1p4wx4WyJQtVPW+y/SLyXuB3gbfp2Ai//cDqgsNW+TRKpB8BlohIzJcuCo/PnatDRGJAoz/eLJDpNsZuXNfCBze+ys9T5dZ0qK+Kk4hF8zf53AC1fT3Dk641PR+N0VPNS6Fcb6aB0Qyv9I4ifo6n4XQw56PBjZkPs+0NdT7wt8BvqWrhY+U24Fsi8q+4Bu7TgUdxVdan+55P+3GN4H+oqioiPwUuxrVjXAbcXXCuy4CH/P4HC4LSvJvJjeR4M5PG2FLzVBWuAzHVKpy5rK6ZaXVSLoB2DaTys/yGISRjQjxqixWZY99s2yy+CCSB+32b88Oq+mequltE7gKewlVPfVBVAwAR+RBwH67r7FdVdbc/19XAnSLyT8DjwFd8+leAr4tIO9CNCzALohL10seDmT7dl7rJL6YBaTPNSy6AjmbduI8whBBleV2VjZcwx4VZBQtVffUk+z4FfKpI+j3APUXS9+J6S01MHwV+fzb5rJTFdFNbaJV8ul9MA9JmmpeJo9+TMWF5XRUN1XGG01kbL2GOebPqOnuimW7Drpma1U01jGTGj/peqAFps8lLbvT7yUuqWdlYRX1VbNE0wBszWxYspmEx3dSOJ4uph9Ns82LjJczxyuaGmgYbZTs3FsN0G5XMi42XMMcjW89imiauQXAi9oYyxhy/bD2LCrGnRjMfrIu2WWyszcKYRabU3Fs2EtwsJAsWxiwyhV20Rdzv3MA+YxaKBQtjFhnrom0WIwsWxiwy1kXbLEYWLIxZZGY61mM262kYU44FC2MWmZkM7LNGcTPXrOusMYvQdLto27xlZq5ZycKY44A1ipu5ZsHCmOOANYqbuWbBwpjjwGKajNEcnyxYGHMcsNluzVyrSAO3iPw18BmgWVUPi1s27wvAhcAw8F5V3eWPvQz4B//Wf1LV23z6G4BbgWrc4kgf9sutLgW+DawBXgTerao9lci3MccTm7fMzKVZlyxEZDXwDuDlguQLcOtunw60Al/yxy4FPg68Cbcq3sdFpMm/50vABwred75Pvwb4iaqeDvzEbxtjjJlHlaiG+hzwt0DhXOebgNvVeRhYIiInAe8E7lfVbl86uB843+9rUNWH1c2ZfjvwroJz3eZf31aQbowxZp7MKliIyCZgv6o+MWHXKcC+gu0OnzZZekeRdIAVqnrAvz4IrJgkP60i0iYibV1dXdP9OsYYY0oo22YhIg8AK4vs+nvg73BVUPPCt2GUXK1JVW8Gbga3+NF85csYY453ZYOFqp5XLF1E/i/gNOAJ157NKmCXiLwR2A+sLjh8lU/bD2yckL7dp68qcjzAIRE5SVUP+Ooqm7/AGGPm2YyroVT1SVVtUdU1qroGV3X0elU9CGwD3iPOOUCfr0q6D3iHiDT5hu13APf5ff0ico7vSfUe4G7/UduAy/zrywrSjTHGzJO5mhvqHly32XZc19n3Aahqt4h8EnjMH3edqnb711cw1nX2Xv8DcD1wl4hcDrwEvHuO8myMMaYEcZ2Pjj/r16/Xtra2hc6GMcYcU0Rkp6qun5huI7iNMcaUZcHCGGNMWRYsjDHGlGXBwhhjTFkWLIwxxpRlwcIYY0xZFiyMMcaUZcHCGGNMWRYsjDHGlGXBwhhjTFkWLIwxxpRlwcIYY0xZFiyMMcaUZcHCGGNMWRYsjDHGlGXBwhhjTFmzDhYi8hciskdEdovIvxSkXysi7SLyjIi8syD9fJ/WLiLXFKSfJiKP+PRvi0jCpyf9drvfv2a2eTbGGDM9swoWIvLbwCbgtap6FvAZn34mcAlwFnA+8O8iEhWRKHATcAFwJnCpPxbgBuBzqvpqoAe43KdfDvT49M/544wxxsyj2ZYs/hy4XlVTAKra6dM3AXeqakpVX8Ctxf1G/9OuqntVNQ3cCWwSEQHeCmz1778NeFfBuW7zr7cCb/PHG2OMmSezDRZnAP/HVw/9l4j8hk8/BdhXcFyHTyuVvgzoVdXshPRx5/L7+/zxRxGRVhFpE5G2rq6uWX41Y4wxObFyB4jIA8DKIrv+3r9/KXAO8BvAXSKytqI5nAZVvRm4GWD9+vW6UPkwxpjjTdlgoarnldonIn8OfE9VFXhUREJgObAfWF1w6CqfRon0I8ASEYn50kPh8blzdYhIDGj0xxtjjJkns62G+g/gtwFE5AwgARwGtgGX+J5MpwGnA48CjwGn+55PCVwj+DYfbH4KXOzPexlwt3+9zW/j9z/ojzfGGDNPypYsyvgq8FUR+SWQBi7zN/LdInIX8BSQBT6oqgGAiHwIuA+IAl9V1d3+XFcDd4rIPwGPA1/x6V8Bvi4i7UA3LsAYY4yZR3K8PqSvX79e29raFjobxhhzTBGRnaq6fmK6jeA2xhhTlgULY4wxZVmwMMYYU5YFC2OMMWVZsDDGGFOWBQtjjDFlWbAwxhhTlgULY4wxZVmwMMYYU5YFC2OMMWXNdm4oY4wx82D7nk627NjLvp5hVjfVsHnDWjaua5m3z7eShTHGLHLb93TysW276RwYZUl1nM6BUT62bTfb93SWf3OFWLAwxphFbsuOvcSjQk0ihoj7HY8KW3bsnbc8WDWUMcYscvt6hllSHR+XVh2P0tEzPC5tLquqrGRhjDGL3OqmGkYywbi0kUzAqqaa/PZcV1VZsDDGmEVu84a1ZAJlOJ1F1f3OBMrmDWvzx8x1VdWsgoWI/LqIPCwiPxeRNhF5o08XEblRRNpF5Bci8vqC91wmIs/5n8sK0t8gIk/699woIuLTl4rI/f74+0WkaTZ5NsaYY83GdS1cd9FZtNRX0TeSoaW+iusuOmtcFdO+nmGq49Fx7ytWVTVTs22z+BfgE6p6r4hc6Lc3Ahfg1t0+HXgT8CXgTSKyFPg4sB5QYKeIbFPVHn/MB4BHgHuA84F7gWuAn6jq9SJyjd++epb5NsaYY8rGdS2Ttj+sbqqhc2CUmsTYbX1iVdVszLYaSoEG/7oReMW/3gTcrs7DwBIROQl4J3C/qnb7AHE/cL7f16CqD/s1vG8H3lVwrtv869sK0o0xxnhTqaqajdmWLP4SuE9EPoMLPL/p008B9hUc1+HTJkvvKJIOsEJVD/jXB4EVpTIjIq1AK8Cpp546g69jjDHHpo3rWrgO13bR0TPMqgr3hiobLETkAWBlkV1/D7wN+IiqfldE3g18BTivIjkrQlVVRHSS/TcDNwOsX7++5HHGGHM8KldVNRtlg4Wqlrz5i8jtwIf95neAW/zr/cDqgkNX+bT9uDaNwvTtPn1VkeMBDonISap6wFdXzd+QRWOMMcDs2yxeAX7Lv34r8Jx/vQ14j+8VdQ7Q56uS7gPeISJNvlfTO4D7/L5+ETnH94J6D3B3wblyvaYuK0g3xhgzT2bbZvEB4AsiEgNG8e0FuN5MFwLtwDDwPgBV7RaRTwKP+eOuU9Vu//oK4FagGtcL6l6ffj1wl4hcDrwEvHuWeTbGGDNN4jofHX/Wr1+vbW1tC50NY4w5pojITlVdPzHdRnAbY4wp67gtWYhIF67aaiqWA4fnMDszZfmaHsvX9Fi+pudEydevqGrzxMTjNlhMh4i0FSt2LTTL1/RYvqbH8jU9J3q+rBrKGGNMWRYsjDHGlGXBwrl5oTNQguVreixf02P5mp4TOl/WZmGMMaYsK1kYY4wpy4KFMcaY8lT1hP3BLbD0DG5akmvm6DNWAz8FngJ2Ax/26Utx63k85383+XQBbvR5+gXw+oJzXeaPfw64rCD9DcCT/j034qsXp5C3KPA48EO/fRpu8al24NtAwqcn/Xa737+m4BzX+vRngHdW4toCS4CtwB7gaeDcRXK9PuL/DX8J3AFULcQ1A76Km1DzlwVpc359Sn1GmXx92v87/gL4PrBkptdhJte6VL4K9v01bl2e5Yvhevn0v/DXbDfwL/N9vUr+3U335ne8/OBulM8Da4EE8ARw5hx8zkm5PzigHngWOBO3quA1Pv0a4Ab/+kLcvFgCnAM8UvCHt9f/bvKvczeER/2x4t97wRTz9lfAtxgLFncBl/jXXwb+3L++Aviyf30J8G3/+kx/3ZL+D/N5f11ndW1xi1y9379O4ILHgl4v3PoqLwDVBdfqvQtxzYANwOsZf1Oe8+tT6jPK5OsdQMy/vqEgX9O+DtO91pPly6evxk1u+hJjwWKhr9dvAw8ASb/dMt/Xq+T/A5W+OR4rP7in1fsKtq8Frp2Hz70beDvuSeAkn3YS8Ix/vQW4tOD4Z/z+S4EtBelbfNpJwJ6C9HHHTZKPVcBPcLMF/9D/oR9m7H/s/PXx/0Od61/H/HEy8ZrljpvNtcWtNOdx1AAABVxJREFUuPgCE572F8H1yi3ctdRfgx/iVn5ckGsGrGH8TWbOr0+pz5gsXxP2/d/AN4t9v3LXYSZ/n+XyhSu9vhZ4kbFgsaDXC3eDP6/IcfN6vYr9nMhtFqVW7ZszIrIGeB2u6FdqBcBKrjI4mc8DfwuEfnsZ0Kuq2SLnyX+239/nj59uXqfiNKAL+JqIPC4it4hILQt8vVR1P/AZ4GXgAO4a7GRxXDOYn+sz5VUrS/hTxmaTnm6+ZvL3WZKIbAL2q+oTE3Yt9PU6A/g/IvKIiPyXiPzGDPNV0esF1sA9b0SkDvgu8Jeq2l+4T12I13nMy+8Cnaq6c74+cxpiuKL5l1T1dcAQrgifN9/XC8Cvv7IJF8xOBmpxdcWLznxcn+l+hoj8PZAFvjlnmZp6XmqAvwM+Nl+fOY3rFcOVXs8BrsItzyBzmbepOpGDRanV/CpOROK4QPFNVf2eTz7kV/5jwgqAk60yWCq91CqDpbwZuEhEXgTuxFVFfQFY4tcmmXie/Gf7/Y3AkRnkdSo6gA5VfcRvb8UFj4W8XuCWC35BVbtUNQN8D3cdF8M1g/m5PqU+Y1Ii8l7gd4E/8jfNmeTrCNO/1qW8Chf0n/D/D6wCdonIyhnkq9LXqwP4njqP4kr+y2eQr0peL6dcPdXx+oOL4HtxfzS5hqGz5uBzBLgd+PyE9E8zvvHrX/zr32F8A9ujPn0pri6/yf+8ACz1+yY2sF04jfxtZKyB+zuMbxC7wr/+IOMbxO7yr89ifKPbXlyD26yuLfDfwK/61//or9WCXi/gTbjeKTX+fbfheq0syDXj6LruOb8+pT6jTL7Ox/UEbJ5w3LSvw3Sv9WT5mrDvRcbaLBb6ev0ZblE4cFVS+/x55/V6Fb1OlbopHos/uJ4Pz+J6E/z9HH3GW3DFz18AP/c/F+LqCH+C61b3QMEfngA3+Tw9Cf9/e/fzElUUhnH8+4iUm1q0a9PCQFr0Q8EIqoX1P4QLISw3LYKgTYtW/QUtDIk2LapVLYQ2kYuCKGKsSCxQmsggci0GYZGnxXmHrsVwbNSZhc8HBOfOue/cebl3XodzfC+DlVjnycvd6sC5yvZB8nLOj8AN1rkUNPYd4k+x6I0Tvx4nWmNFRk88rsfzvZX9r8brzlNZVbSR3AL9wKvI2WRcnB3PF3CNvKTxHXAnLty254y8bHcR+En+S3SsHflp9hqF46qTP/Aa5/7NVvPQSq6bHddfzy+wdulsJ/O1A7gb8d4Ap9udr2Y/bvdhZmZF23nOwszM1snFwszMilwszMysyMXCzMyKXCzMzKzIxcLsP0j61uljMOsEFwszMytysTBrgaQhSU8lPZA0J+leo4ePpKOSXkiakVSTtEtSj6TbkmajQeKpGDsqaVLSlKQFSRclXY4xLyXtiXH7JT2S9FrSM0kHOvn+bfvpLg8xsyYGyG0YvgLPgROSauQbywynlKYl7Qa+A5fI/eQOxQf9Y0l9EedgxOoh/1ftlZTSgKTrwFlyh+BbwIWU0gdJx4AJck8vs7ZwsTBrXS2l9AVA0ltyn58lYDGlNA2QosOwpJPAeGybk/SZ3PsH4ElKaRlYlrQEPIzts8Dh6Fh8HLhfaUC6c4vfm9kaLhZmrVup/P6L1q+napzVyuPViNlFvjdBf4vxzTbMcxZmm2se2Nu4aU3MV3STO+mOxLY+YF+MLYpvJ58knYn9JenIVhy8WTMuFmabKKX0AxgGxiXNAFPkuYgJoEvSLHlOYzSltNI80j9GgLGI+Z58IyaztnHXWTMzK/I3CzMzK3KxMDOzIhcLMzMrcrEwM7MiFwszMytysTAzsyIXCzMzK/oNz1V7T5ZVtWgAAAAASUVORK5CYII=\n" - }, - "metadata": { - "needs_background": "light" - } - }, - { - "output_type": "stream", - "name": "stdout", - "text": [ - "---------------------------\n", - "\n", - "Resultados do Polinomial de Grau: 2\n", - "\n", - "Resultado do conjunto de treino - Grau 2 :\n", - "As variáveis explicativas do meu modelo explicam 81.69 % das variações na renda dos clientes.\n", - "O erro médio absoluto do modelo é: 6953.15\n", - "O erro médio quadrático do modelo é: 85098902.1\n", - "A raiz quadrada do erro médio quadrático é: 12704.330739940186\n", - "Acurácia: 0.75\n", - "\n", - "Veja o comportamento dos resíduos:\n" - ] - }, - { - "output_type": "display_data", - "data": { - "text/plain": [ - "
" - ], - "image/png": 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\n" - }, - "metadata": { - "needs_background": "light" - } - }, - { - "output_type": "stream", - "name": "stdout", - "text": [ - "\n", - "Resultado do conjunto de teste - Grau 2 :\n", - "As variáveis explicativas do meu modelo explicam 65.3 % das variações na renda dos clientes.\n", - "O erro médio absoluto do modelo é: 8150.09\n", - "O erro médio quadrático do modelo é: 160362399.84\n", - "A raiz quadrada do erro médio quadrático é: 12704.330739940186\n", - "Acurácia: 0.65\n", - "\n", - "Veja o comportamento dos resíduos:\n" - ] - }, - { - "output_type": "display_data", - "data": { - "text/plain": [ - "
" - ], - "image/png": 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\n" - }, - "metadata": { - "needs_background": "light" - } - }, - { - "output_type": "stream", - "name": "stdout", - "text": [ - "---------------------------\n", - "\n", - "Resultados do Polinomial de Grau: 3\n", - "\n", - "Resultado do conjunto de treino - Grau 3 :\n", - "As variáveis explicativas do meu modelo explicam 86.68 % das variações na renda dos clientes.\n", - "O erro médio absoluto do modelo é: 6112.91\n", - "O erro médio quadrático do modelo é: 61903278.53\n", - "A raiz quadrada do erro médio quadrático é: 12704.330739940186\n", - "Acurácia: 0.75\n", - "\n", - "Veja o comportamento dos resíduos:\n" - ] - }, - { - "output_type": "display_data", - "data": { - "text/plain": [ - "
" - ], - "image/png": 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\n" - }, - "metadata": { - "needs_background": "light" - } - }, - { - "output_type": "stream", - "name": "stdout", - "text": [ - "\n", - "Resultado do conjunto de teste - Grau 3 :\n", - "As variáveis explicativas do meu modelo explicam 51.88 % das variações na renda dos clientes.\n", - "O erro médio absoluto do modelo é: 8368.21\n", - "O erro médio quadrático do modelo é: 222418278.71\n", - "A raiz quadrada do erro médio quadrático é: 12704.330739940186\n", - "Acurácia: 0.65\n", - "\n", - "Veja o comportamento dos resíduos:\n" - ] - }, - { - "output_type": "display_data", - "data": { - "text/plain": [ - "
" - ], - "image/png": 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\n" - }, - "metadata": { - "needs_background": "light" - } - }, - { - "output_type": "stream", - "name": "stdout", - "text": [ - "---------------------------\n", - "\n", - "Resultados do Polinomial de Grau: 4\n", - "\n", - "Resultado do conjunto de treino - Grau 4 :\n", - "As variáveis explicativas do meu modelo explicam 91.45 % das variações na renda dos clientes.\n", - "O erro médio absoluto do modelo é: 4725.18\n", - "O erro médio quadrático do modelo é: 39752173.18\n", - "A raiz quadrada do erro médio quadrático é: 12704.330739940186\n", - "Acurácia: 0.75\n", - "\n", - "Veja o comportamento dos resíduos:\n" - ] - }, - { - "output_type": "display_data", - "data": { - "text/plain": [ - "
" - ], - "image/png": 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\n" - }, - "metadata": { - "needs_background": "light" - } - }, - { - "output_type": "stream", - "name": "stdout", - "text": [ - "\n", - "Resultado do conjunto de teste - Grau 4 :\n", - "As variáveis explicativas do meu modelo explicam -284738667.91 % das variações na renda dos clientes.\n", - "O erro médio absoluto do modelo é: 1472176.41\n", - "O erro médio quadrático do modelo é: 1315989882764594.5\n", - "A raiz quadrada do erro médio quadrático é: 12704.330739940186\n", - "Acurácia: 0.65\n", - "\n", - "Veja o comportamento dos resíduos:\n" - ] - }, - { - "output_type": "display_data", - "data": { - "text/plain": [ - "
" - ], - "image/png": 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ba3/C+I0r+tqNj93E+M339bfX3Mj4Lb/pbz/6Q+qf+W1fe9Ij11G/9cH+9sPfZ9y2/oOCyU1LGffsI1mjpztrb1+Ttbs7mNy0lLodawFQV3vWbs2OOdSxL2vvXJ+1X26jsWkp1YM89qr2v5g9VnZtpr0LqvY+z+SmpXS/sIWW3e20v9DCxp9+i68s+wUrmltZv349hUKB9evXs6K5la8s+wUbbrmatudaONDZzb7tG3nqlqv56g/uZUVzK6tXr6ZQKNDS0gLAqlWrKBQK7NixA4D777+fQqHA889nj9WVK1dSKBTYvXs3AMuXL6dQKLB3714A7r77bgqFAu3t2WPxzjvvpFAo0NWVvUu9/fbbKRQKfWN56623cv755/e1b7rpJi644IK+9g033MBFF13U177uuuu4+OKL+9rLli1jwYIFfe0lS5ZwySWX9LWvvfZaLrvssr72NddcwxVXXNHXXrhwIVdeeWVf++qrr+bqq6/ua1955ZUsXLiwr33FFVdwzTXX9LUvu+wyrr322r72JZdcwpIlS/raCxYsYNmyZX3tiy++mOuu639uXXTRRdxwww2saG5l/uIHeMeHP8GHLryKFc2tAJx//vncemv23FnR3MqffeQcTv3sVcxf/AC/WvvssHPvcEoJ6uOBrUXtlnzdQSQVJDVJaurs7Cxht4Nr7+qhJwIpXzHE0XEEVAk6unvo7PGhtKVhz4Gu7GCjChat3HjQtkUrN1JTBV09QVWVqKmqQojunqCmWr/X3ypjw3NtXHrbOlrb2qmpEm0vd3Lpbev6whqykL70tnV0dPfQUFtNa1s7X7/jcXbvP7IcLFbK1MdHgTMi4tN5+y+Bd0bE5wa7zZFOfcxf/ACPbN1F9EBVlXi5q5vD5e+Eumo6unuoq85ed/Z3dA+V7WZj4o+Pn0xEsOdAJ/d96fS+9e+9cjlTxtey/rk2qquEEBFBdwRvfG3j7/W3ypi/+AFa29ppqOs/nbe/o4sZjfXcUDi15D6lONzURyknE7cBs4raM/N1o6p3vm7rrv00jquhtkoc6O4hegId5nbFc9SN9TWeo7ZkVOUP3AOd3cyc2nDQtllTG2hta6euuoqunuzdYwTUVVcN2N8qY+uu/UwZX3vQuvG11bTs2j+sPkeqlKmPh4A3SDpBUh1wDnDbELcZlt63Dq1t7UwZX0tHdw/1tdW8rrEOSdRUVzFzSj2T6g9+XZk2sY5J42uZ/ZqJXHj6H3HCtIn0BLxhxkRmTR1PbZX6p0/MxthrJtSyv6OLzu7gvNPmHLTtvNPm0NkdTBpfQ09P0NXTQw9BY33NgP2tMmZNbeBAZ/dB6w59IS2lz5Ea8og6IrokfQ64i+zyvO9FxLpRq4Bsvq62Wn1vHXq/z2is59cLSn/rcMHQXewIzV90P7/dtKvSZYyZI7nqQxIzGusHvIpj3kkzuJzssd/ZnV31UVctTpg20Vd9JOS80+Zw6W3r2N/Rxfjaag50dv/eC2kpfY7UkHPUIzHcOere+ToVHf4ONK9nZjbWeqdlW3btZ+Ygl1uW0mcoRzpHXXa983XFk/GepzOzFMw7acaQoVtKnyORxJ+Q987X7e/oIiIGndczMzsaJRHU806aweVnncyMxnr2HOhkRmM9l591sufpzMxIZOoDyv/WwczslSqJI2ozMxucg9rMLHEOajOzxDmozcwS56A2M0tcWf4yUdJOYEsJXacBz496AUfOdQ2P6xoe1zV8qdY2mnX9YURMH2hDWYK6VJKaBvuTyUpyXcPjuobHdQ1fqrWNVV2e+jAzS5yD2swscZUO6sUVvv/BuK7hcV3D47qGL9XaxqSuis5Rm5nZ0Cp9RG1mZkNwUJuZpS4iKvIFnAGsBzYAXy7D/mcB9wKPA+uAC/P1xwD3AE/l36fm6wV8J6/nd8Dbi/b113n/p4C/Llr/J8Bj+W2+Qz6VVGJ91cAjwB15+wRgVb6vHwF1+fpxeXtDvn120T4W5OvXAx880rEFpgA3A83AE8C7Uhgv4KL8d7gWuAGor9R4Ad8DWoG1RevKPkaD3ccQdf1z/rv8HXArMGWkYzGS8R6srqJtfw8EMC2F8crXfz4fs3XAVWM9XoM+7oYTfqP1RRZSTwNzgDpgDfCmUb6PY3t/0UAj8CTwJuCq3gEFvgxcmS+fCfw8f7CcCqwq+oVvzL9PzZd7n4gP5n2V3/ZDw6jvC8AP6Q/qHwPn5MvXAp/Nl88Hrs2XzwF+lC+/KR+3cfmD4ul8XEc8tsD3gU/ny3VkwV3R8QKOBzYB44vG6dxKjRdwGvB2Dg7Eso/RYPcxRF3/CajJl68sqmvYYzHc8T5cXfn6WWT/h3UL/UFd6fH6c+CXwLi8PWOsx2vQx91ohuMwQupdwF1F7QXAgjLf5/8DPkD26ndsvu5YYH2+vAiYX9R/fb59PrCoaP2ifN2xQHPR+oP6DVHLTOBXwOnAHfmD7Hn6n1R945M/mN+VL9fk/XTomPX2G+nYApPJAlGHrK/oeJEF9VayJ2lNPl4frOR4AbM5+Ale9jEa7D4OV9ch2/4LcP1AP+NQYzGSx+dQdZG9c3srsJn+oK7oeJGF6/sH6Dem4zXQV6XmqHuffL1a8nVlIWk2cArZW43XRsT2fNMO4LVD1HS49S0DrC/FQuCL9P9z69cAuyOia4B99d1/vn1P3n+49Q7lBGAnsFTSI5KWSJpAhccrIrYB3wSeAbaT/fyrqfx4FRuLMRrsPkr1t2RHnCOpaySPz0FJOhvYFhFrDtlU6fE6EfgzSask/bukPx1hXaM6XnAUnEyUNBH4CfB3EfFS8bbIXtZijOv5MNAaEavH8n5LUEP2VvC7EXEKsI/sLWOfCo3XVOBssheS44AJZPOCSRqLMRrufUj6CtAFXF+2okqvpQH4R+DSsbrPYYxXDdk7t1OBi4EfS1I5aytVpYJ6G9kcVa+Z+bpRJamWLKSvj4hb8tXPSTo2334s2QmFw9V0uPUzR/AzvAc4S9Jm4Eay6Y9vA1Mk9f5rtOJ99d1/vn0y8MII6h1KC9ASEavy9s1kwV3p8Xo/sCkidkZEJ3AL2RhWeryKjcUYDXYfhyXpXODDwCfywBpJXS8w/PEezOvJXnTX5M+BmcDDkl43grpGe7xagFsi8yDZO95pI6hrNMcrM9TcSDm+yF65NpL9wnon4U8e5fsQ8G/AwkPW/zMHn2S4Kl/+Cw4+kfFgvv4YsrnbqfnXJuCYfNuhJzLOHGaN8+g/mXgTB598OD9f/u8cfPLhx/nyyRx8gmMj2cmNEY8tcB/wxnz56/lYVXS8gHeSnYFvyG/3fbIz8xUbL35/brPsYzTYfQxR1xlkVz1NP6TfsMdiuON9uLoO2baZ/jnqSo/XZ4DL8+UTyaYoNNbjNeA4jUYojuSL7Azvk2RnTb9Shv2/l+ztzu+AR/OvM8nmg35FdtnOL4t+4QL+d17PY8Dcon39LdnlNBuAvylaP5fskrGngWsYxuV5+e3n0R/Uc/IH3Yb8l9x75rk+b2/It88puv1X8vteT9EVFCMdW+BtQFM+Zj/NnxQVHy/gMrJLptYC1+VPmIqMF9nlgduBTrIjsE+NxRgNdh9D1LWBLGx6H//XjnQsRjLeg9V1yPbNHHx5XiXHqw74Qb6/h4HTx3q8Bvvyn5CbmSXuVX8y0czslc5BbWaWOAe1mVniHNRmZolzUJuZJc5Bba8YkvZWugazSnBQm5klzkFtrziS5klaIelmSc2Sru/9TAZJfyrpfklrJD0oqVFSvaSlkh7LP3Dqz/O+50r6qaR7JG2W9DlJX8j7PCDpmLzf6yX9QtJqSfdJOqmSP78dfWqG7mKWpFPI/rT3WeA3wHskPUj2oewfj4iHJE0CDgAXkn02zx/nIXu3pBPz/bw531c92V+LfSkiTpH0v4C/Ivukw8XAZyLiKUnvBP6V7DNazMaEg9peqR6MiBYASY+SfW7DHmB7RDwEEPmnJUp6L/Av+bpmSVvIPssB4N6IaAPaJO0Bbs/XPwa8Jf/0xXcDNxV9kNq4Mv9sZgdxUNsr1ctFy92M/LFcvJ+eonZPvs8qss8WftsI9292xDxHba8m64Fjez/wPZ+friH7VMBP5OtOBP4g7zuk/Kh8k6SP5beXpLeWo3izwTio7VUjIjqAjwP/ImkN2T81rSebU66S9BjZHPa5EfHy4Hv6PZ8APpXvcx3ZPzIwGzP+9Dwzs8T5iNrMLHEOajOzxDmozcwS56A2M0ucg9rMLHEOajOzxDmozcwS9/8Bzik/xWuOMXQAAAAASUVORK5CYII=\n" - }, - "metadata": { - "needs_background": "light" - } - }, - { - "output_type": "stream", - "name": "stdout", - "text": [ - "---------------------------\n", - "\n", - "Resultados do Polinomial de Grau: 5\n", - "\n", - "Resultado do conjunto de treino - Grau 5 :\n", - "As variáveis explicativas do meu modelo explicam 18.85 % das variações na renda dos clientes.\n", - "O erro médio absoluto do modelo é: 12516.47\n", - "O erro médio quadrático do modelo é: 377221553.92\n", - "A raiz quadrada do erro médio quadrático é: 12704.330739940186\n", - "Acurácia: 0.75\n", - "\n", - "Veja o comportamento dos resíduos:\n" - ] - }, - { - "output_type": "display_data", - "data": { - "text/plain": [ - "
" - ], - "image/png": 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\n" - }, - "metadata": { - "needs_background": "light" - } - }, - { - "output_type": "stream", - "name": "stdout", - "text": [ - "\n", - "Resultado do conjunto de teste - Grau 5 :\n", - "As variáveis explicativas do meu modelo explicam -13816964.21 % das variações na renda dos clientes.\n", - "O erro médio absoluto do modelo é: 1716501.42\n", - "O erro médio quadrático do modelo é: 63858942879285.67\n", - "A raiz quadrada do erro médio quadrático é: 12704.330739940186\n", - "Acurácia: 0.65\n", - "\n", - "Veja o comportamento dos resíduos:\n" - ] - }, - { - "output_type": "display_data", - "data": { - "text/plain": [ - "
" - ], - "image/png": 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\n" - }, - "metadata": { - "needs_background": "light" - } - }, - { - "output_type": "stream", - "name": "stdout", - "text": [ - "---------------------------\n", - "\n", - "Resultados do Polinomial de Grau: 6\n", - "\n", - "Resultado do conjunto de treino - Grau 6 :\n", - "As variáveis explicativas do meu modelo explicam 59.23 % das variações na renda dos clientes.\n", - "O erro médio absoluto do modelo é: 7858.94\n", - "O erro médio quadrático do modelo é: 189490829.76\n", - "A raiz quadrada do erro médio quadrático é: 12704.330739940186\n", - "Acurácia: 0.75\n", - "\n", - "Veja o comportamento dos resíduos:\n" - ] - }, - { - "output_type": "display_data", - "data": { - "text/plain": [ - "
" - ], - "image/png": 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\n" - }, - "metadata": { - "needs_background": "light" - } - }, - { - "output_type": "stream", - "name": "stdout", - "text": [ - "\n", - "Resultado do conjunto de teste - Grau 6 :\n", - "As variáveis explicativas do meu modelo explicam -100390904749.28 % das variações na renda dos clientes.\n", - "O erro médio absoluto do modelo é: 130211783.26\n", - "O erro médio quadrático do modelo é: 4.6398112934485344e+17\n", - "A raiz quadrada do erro médio quadrático é: 12704.330739940186\n", - "Acurácia: 0.65\n", - "\n", - "Veja o comportamento dos resíduos:\n" - ] - }, - { - "output_type": "display_data", - "data": { - "text/plain": [ - "
" - ], - "image/png": 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\n" - }, - "metadata": { - "needs_background": "light" - } - }, - { - "output_type": "stream", - "name": "stdout", - "text": [ - "---------------------------\n", - "\n", - "Resultados do Polinomial de Grau: 7\n", - "\n", - "Resultado do conjunto de treino - Grau 7 :\n", - "As variáveis explicativas do meu modelo explicam 6.56 % das variações na renda dos clientes.\n", - "O erro médio absoluto do modelo é: 11323.96\n", - "O erro médio quadrático do modelo é: 434355069.57\n", - "A raiz quadrada do erro médio quadrático é: 12704.330739940186\n", - "Acurácia: 0.75\n", - "\n", - "Veja o comportamento dos resíduos:\n" - ] - }, - { - "output_type": "display_data", - "data": { - "text/plain": [ - "
" - ], - "image/png": 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\n" - }, - "metadata": { - "needs_background": "light" - } - }, - { - "output_type": "stream", - "name": "stdout", - "text": [ - "\n", - "Resultado do conjunto de teste - Grau 7 :\n", - "As variáveis explicativas do meu modelo explicam -1088037024738.78 % das variações na renda dos clientes.\n", - "O erro médio absoluto do modelo é: 401499595.75\n", - "O erro médio quadrático do modelo é: 5.028629319674215e+18\n", - "A raiz quadrada do erro médio quadrático é: 12704.330739940186\n", - "Acurácia: 0.65\n", - "\n", - "Veja o comportamento dos resíduos:\n" - ] - }, - { - "output_type": "display_data", - "data": { - "text/plain": [ - "
" - ], - "image/png": 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EmfB5x8/0kM44588tz7bYD7d20pd2MhmHnPFFB3YcamXtpj08s+8kzW09mMF51WUcP9NNOuOkHdJp59XmDkrixme+/0q2lT+eVnGhLso8XMt+6C8TEQ12SkFM9Of/cM8DuPH+HbT1pHGCno/+d2nu/VylceP8uWXZOd27j7ef9VyAkriRdqgujdPVl6EnlRlmbwNiBn/262/mwR1HScaN8mSclvYeWjv7qC5L8OaF1cO+1tw+8skMbq5YvZl55cmBbiDA3Tnd1VeQAVmJFg12niOK0Z86dGCv/+f/tUdO8cy+kxxu7QR3ms700JtxDFg0v5xF88rYdqCV/rbE0VNd7DjUSlVJnI7edDYA5wbikZodPWnn4MmuswL30O1700HOqa5UXq8t43D3U40sXlBBRUmCM+Hcc4DOntSgQVhg0ADrwupSkrHYsIOb+f6fCtWyl6mVz/9zKj+bBWmRm9nVwBogDmxw98+Mtv1EWuRDK+GXly7gmX0n2XP8DH1ppyQRoyxuHG/rpS/n1Ls5ZQkuu3Budvv+VfA6etO8frqblDsWbj5620zkbL9w0dzg5KLOXk509J31xREzuHRhFX/1wZ8fFMTzba2v3bSHf93yKumMU5qIUV2WoCQR17TFGSSf/2ehfqGN1CKf9GCnmcWBfwU+CLwVWGVmb53sfnMNHfA5cKKdNZsbeeXYac50p+jqS9PS3suR0z2DgjjAme4ULx09xZrNjexvCVa+29vUzuHWLvoyjnsQwBXEZSJebWrjTFcvLcMEcQiWBDhwsnPQAGW+p+tvaWjiwR1HWVCZpCRudKfStHb2ce07L1IQn0Hy+X9O9RINhZi18m6g0d33uXsv8E3gI6M94eDBgzzyyCMApFIp6urqeOyxxwDo7u6mrq6OJ554AoD29nZuu+VGeO1lKkoSeE8H3U/dQ0lzA2e6U8R72ql6/sskW/YGL6j7NHPrN5I88WqQ7jxJ/McbSLbup607xYnXj1L1/EYSpw4BEG8/ztz6jSROHw3SbceYW7+ReNsxABKnjwbp9uNB+tShIN3REqRbDzC3fiOxzpMAJE+8GqS7Twfplr3Mrd+I9bQBUNK8O0j3dgTppp8F6VR3kH79ZebWb4R08PO99NjOIJ0JRu1KX3shSIdKj9YzZ8dXsumyw88x54X7BtKHnqH6xW9k0+UHf0z1zm8OpA/8iOqXHhhI79tC1cvfyaYrXt1M1a6HBtKNT1L1ysMD6T2PU9nwaDZdufv7VO7+/kC64VEq9jyeTVe98jAVjU8OpHc9RMWrmwfSL3+H8n1bsunqlx6g/MCPBtI7v0n5wR8PpF/8BmWHnsmm57xwH2WHnxtI7/gKpUcHfv3Nrd9I6WsvBIlMOkgf2xmk073Mrd9IyesvA2Cp7iDd9LMg3dsRpJt3B+meNpLPfIkzB4PHh3vvzanfSLppH72pNF94+Bnq6uo4uPdnlCfj9LUe48QPvkBvyyHKk3EO7W+krq6O3buD/a958Cl6tnyRuX2tvGlhNZfETzJvx0Z+uOMVALZv305dXR1HjhwB4Nlnn6Wuro7XX38dgJ/85CfU1dXR0hK8V7du3UpdXR2nTp0CYPPmzdTV1dHe3g7AE088QV1dHd3dwXvxscceo66ujlQq6IZ65JFHqKury9blQw89xCc/+cls+oEHHuDGG2/Mpu+//35uvvnmbPq+++7jlltuyabvvfdebr311mx6w4YN3Hbbbdn0Pffcw+23355N33333dx5553Z9F133cXq1auz6c9//vN8/vOfz6ZXr17NXXfdlU3feeed3H333dn07bffzj333JNN33bbbWzYsCGbvvXWW7n33nuz6VtuuYX77hv4bN18883cf//92RlKJzfdQ0fD00AwQ+nV793FQw8Fn53DrZ10bv4inY3PAuCZNJ2bv8iBF4Pth4t7dXV1bN4cfDZOnTo1qO6HKkQgvwg4nJM+EuYNYmZ1ZlZvZvV9fX3jOkB3KpNd6AiCVo5Z0H9pNsoTc8TCVe+GtthFJmust5QDTW09HGwJAubC6rJhT9c/r7psUN7xtm5iQ97gMTNeP9M16TJL4Yy0/EJpzvTTxfMryAzpxs64s6CypCBlmHQfuZldC1zt7teH6d8H3uPufzLSc8bbRz709O19ze30pjOkM04yFiMWs7Mqcqhk3LJfBp05A2kik1WRjNOdSo8Z0GMGF84rz/t0/XyWNpDimxV95MBRYHFOelGYVzBDTxaZU54g48FAZgYnlRm9h7uqNFh6tLosQU1VyVnrSYtM1LLzKqkoiY0YxHPfahmHeeXJYG10Bk7XX1hdNuwHOp8TqaT4chdnG+n/mc82k1GIFnkC2AP8OkEAfx74b+6+a6TnTGbWSv8JJf2zUPYeP0PvOGatHGntpFKzVmatkeaZT4Vl51XS2ec0nemmJ5UZ9rhD58C/7aK5QP4t66Hve52ef24bqUVeqOmH1wB3EUw//LK73zna9johaGbIJ0gU4uf9qvXbOHCinTNdqewqg3PKEyx5QxX3172XLQ1NrP5BA/taggHg6tLgi7Y37dmLGwPZaXhxC+aDD33nLppXxn9dvpivbjvIma4+etPev4xKdizFzEiNc5wkBixaUDFi18ehk53EY4ZhZDJObyaTnRtflozR3Rc0EWIG8ZgVfJVFOXdM6QlB7v4Y8Fgh9iXTZ+WyhWO27vJZuXAsh1s7eUNlKTVVA4N57p7dRz7lAPjFRfNYt3UfOw61UpoIzuSsLgvK1v/lcuNVl2a3y/211pvKUFkapzQR5+CJwWWPG6SGie0WPlaajGeniuWWs79uSuIxUhnPXkrOCIJ2xiEVdqM4wUBlPBZ8kRw91c2bF1blXYcio9GZnTKqQpxZmLuP46e7aOnoJeNB63Ttpj1jXpShX3/AH+609VQ6w45DraxYvXnQqf79J5HVVJZwoqOXihKjPBnLdr+lM57tUuvfW25Mt5hRU1U67JdX/+uqrS7ltVPdZHDcgy+OqtIEtVWltPekaGnvoTeVIW4W7Dw8QDGWx5DZSYFcRnXDFUv59MO76OxNDRptH8+AW/8+jrR20NqZc2q8O2s2NwLkHczh7C+Xtu4+jp7qJpGzRMAtD+7Egbnlyex64E5wvc5EPEZXKhWMjdjA9MFkOKup/+LMABfOLWdOeZKW9m5Od6Z4y98Gc+SX1lTywbedn12D5YK5pRw/00PKnaULKgedyTncBSzOryod84LNIvnSMrYyqkKMtvfv40x3ELhiFgTUkkScmMGGp/ePq0xDZ3O8fjo4geW86rLsWXNt3Snae1KDzqSbW56krbuPnlTQ3ZFh6BzwoHskEbOwSyU4Jb6lvZvjZ3ro7kvjHrS69za1c9+2g1z7zouyl5K7/I3z+dIfvIsf3Pyrg+pn8fwKEvEYS2urWHb+HJbWVpGIx7ReihSMWuQypnz7sMfaB0BpwojZQPshZoy7ZTp0yVsHLppXxpycvvxUJjOo6wWC7pcjp7pJxo1EzLILaPX3YfemndI41FaXkEo7b6gs4XRXHx09aeIGsfCcBQBzp607xTP7To456FuIXzUio1Egl2lTWRIEsdx5/P3Lzo5X7pdL/+yRXIlYbPAkboK1ywHiZtm53P1iBiWJGO6w5A1Vg2bwrFi9ObhUW84TzMIvhjwGffNZj11kMhTIZdpcv+Ji1mxuJJXJZGd1ZJzs9MKJGq7FW12WwGFwKziToTQM1p4zuds9ODvz4ppKTnf1ndXCXjy/gpb2HjwzMI3RPfiyyLd7pBC/akRGoj5ymTY3XnUpN115CeXJOKlMMI3xpisvGddA53CG68f/3LVv55+uffugvDfXVjG/Ikn/qTtOuG4PwSXlRpqNc8MVS6kqTZB2J53JhDenuiyh7hGZEXSFIDln9K930RsuB9s/6FlTlWROecmoa19saWgadNGIpTWVfOrqZWply7TSFYLknJfbV50Ml2owM9p7gpOJRuu37u8ayb3ASf9a0grmUmwK5HJOmUxf9UiXtLsDBXMpLvWRi+Rpqq/yIjJRapFLpBTj4tL9CrHujMhUUItcImPotVv7uzb6r4U51Ua6EozO0JRiUyCXyCh214Yu9CAzlQK5REb/RW5zTWfXxlRf5UVkotRHLpEx2SV1C9G/rjM0ZSZSi1wiY6JdG1samrj6X37IdV99nhcOtRI3pr1/XWQqKZBLZEyka6N/gPTAyU7iZjhw7HQP6Yxr6qDMGupakUgZb9dG/wBpcK1Pw8zI4DS39XBxTaWmDsqsoBa5zGr9A6Ql8Vh2xUOz4CpAmjoos4UCucxq/XO/a6pKyeBkMsEtbqapgzJrKJDLrNY/QJqIGxfOLcNikHa4uKZSUwdl1lAfucxqQ6/Oc/ni+bo6j8w6CuQy62nut8x26loREYk4BXIRkYhTIBcRiTgFchGRiFMgFxGJOAVyEZGIUyAXEYk4BXIRkYib1AlBZvZR4O+Bnwfe7e71hSiUyFRbu2kPG57eT0dvmsqSONevuJgbr7q02MUSmZDJtshfBn4b2FqAsohMi7Wb9rBmcyNdfWkSseAqQ2s2N7J2055iF01kQiYVyN39FXffXajCiEyHDU/vJ2aQiMWIWSz8G+SLRNG09ZGbWZ2Z1ZtZfXNz83QdVuQsHb1pYjY4L2ZBvkgUjRnIzWyTmb08zO0j4zmQu6939+Xuvry2tnbiJRaZpMqSOBkfnJfxIF8kisYc7HT3q6ajICLT5foVF7NmcyOpTIaYBUE840G+SBRpGVs55/TPTtGsFZktzN3H3mqkJ5v9FvAFoBY4Bbzo7h8Y63nLly/3+nrNVBQRGQ8z2+7uy4fmT6pF7u4PAQ9NZh8iIjI5OrNTRCTiFMhFRCJOg51SVFsamli3dR+HWztZPL9CF0YWmQC1yKVotjQ08emHd9HU1s288iRNbd18+uFdbGloKnbRRCJFgVyKZt3WfSTjRkVJArPgbzJurNu6r9hFE4kUBXIpmsOtnZQnB59NWZ6Mc6S1s0glEokmBXIpmsXzK+jqG7y+SVdfmkXzK4pUIpFoUiCXornhiqX0pZ3O3hTuwd++tHPDFUuLXTSRSFEgl6JZuWwhd3z4MhZWl3G6q4+F1WXc8eHLNGtFZJw0/VCKauWyhQrcIpOkQC4yQZoDLzOFulZEJkBz4GUmUSAXmQDNgZeZRIFcZAI0B15mEgVykQnQHHiZSRTIRSZAc+BlJlEgF5kAzYGXmUTTD0UmSHPgZaZQi1xEJOIUyEVEIk6BXEQk4hTIRUQiToFcRCTizN2n/6BmzcDBPDevAVqmsDgTpXKNj8o1PirX+Jwr5fo5d68dmlmUQD4eZlbv7suLXY6hVK7xUbnGR+Uan3O9XOpaERGJOAVyEZGIi0IgX1/sAoxA5RoflWt8VK7xOafLNeP7yEVEZHRRaJGLiMgoFMhFRKLO3WfkDbga2A00An81RcdYDDwF/AzYBdwU5i8AngT2hn/nh/kGrA3L9FPgnTn7+ni4/V7g4zn5vwS8FD5nLWF3Vh5liwMvAI+G6YuBZ8P9fAsoCfNLw3Rj+PiSnH3cGubvBj5QiLoF5gEPAg3AK8Avz5D6ujn8H74M3A+UFaPOgC8DTcDLOXlTXj8jHWOMcn0u/D/+FHgImDfRephIXY9UrpzH/gJwoGYm1FeY/6dhne0CPjvd9TXi+268wW86bgRB7FVgKVAC7ATeOgXHuaD/zQBUA3uAtwKf7a904K+A1eH9a4Dvh2+o9wLP5rwp9oV/54f3+z+sz4XbWvjcD+ZZtj8HvsFAIP828Lvh/XuAPw7vfxK4J7z/u8C3wvtvDeutNHzTvBrW66TqFvgKcH14v4QgsBe1voCLgP1AeU5dfaIYdQZcAbyTwQFzyutnpGOMUa7fABLh/dU55Rp3PYy3rkcrV5i/GHic4MTBmhlSX78GbAJKw/TC6a6vET8DhQ6OhbgRtPIez0nfCtw6Dcf9f8D7Cb5BLwjzLgB2h/fXAatytt8dPr4KWJeTvy7MuwBoyMkftN0o5VgE/AdwJfBo+CZsYeBDl62f8M3+y+H9RLidDa2z/u0mU7fAXIKAaUPyi11fFwGHCT7IibDOPlCsOgOWMDgATHn9jHSM0co15LHfAr4+3Osbqx4m8ucARngAAAUOSURBVP4cq1wEv/reDhxgIJAXtb4Igu9Vw2w3rfU13G2m9pH3fzD7HQnzpoyZLQEuJ/g5c567Hwsfeh04b4xyjZZ/ZJj8sdwF/C8gE6bfAJxy99Qw+8keO3z8dLj9eMuaj4uBZmCjmb1gZhvMrJIi15e7HwX+CTgEHCOog+3MjDqD6amfkY6Rrz8kaLFOpFwTeX+OyMw+Ahx1951DHip2fV0K/Ccze9bMfmhm75pguQpaX6DBTgDMrAr4DvBn7n4m9zEPvhp9GsvyIaDJ3bdP1zHHIUHwc/OL7n450EHwszRruusLwMzmAx8h+KK5EKgk6JuccaajfsZ7DDP7GyAFfH3KCpV/WSqAvwY+PV3HHEd9JQh+9b0XuAX4tpnZVJYtXzM1kB8l6CPrtyjMKzgzSxIE8a+7+3fD7ONmdkH4+AUEgx6jlWu0/EXD5I/mfcCHzewA8E2C7pU1wDwz6780X+5+sscOH58LnJhAWfNxBDji7s+G6QcJAnsx6wvgKmC/uze7ex/wXYJ6nAl1BtNTPyMdY1Rm9gngQ8DHwoA2kXKdYPx1PZI3EXwh7ww/A4uAHWZ2/gTKVej6OgJ81wPPEfxirplAuQpZX4Gx+l6KcSP45ttH8A/tHyS4bAqOY8BXgbuG5H+OwQMhnw3v/2cGD7Y8F+YvIOg7nh/e9gMLwseGDrZcM47yrWRgsPMBBg+OfDK8/z8ZPDjy7fD+ZQwegNlHMPgyqboFfgS8Jbz/92FdFbW+gPcQzCKoCJ/3FYLZBUWpM87uW53y+hnpGGOU62qCGVu1Q7Ybdz2Mt65HK9eQxw4w0Ede7Pr6I+CO8P6lBF0gNt31NWw9FSooFvpGMEK9h2DU92+m6BgrCH5S/RR4MbxdQ9An9R8EU5M25bwpDPjXsEwvActz9vWHBFOGGoH/npO/nGBK3KvA3eQ5nS587koGAvnS8E3ZGL4J+kfOy8J0Y/j40pzn/0143N3kzP6YTN0C7wDqwzr7XvjBKXp9AbcTTAt7Gbgv/FBNe50RTH08BvQRtOCum476GekYY5SrkSAY9b/375loPUykrkcq15DHDzB4+mEx66sE+Fq4vx3AldNdXyPddIq+iEjEzdQ+chERyZMCuYhIxCmQi4hEnAK5iEjEKZCLiEScArnMGmbWXuwyiBSDArmISMQpkMusY2YrzWyLmT1oZg1m9vX+NTHM7F1m9hMz22lmz5lZtZmVmdlGM3spXAzs18JtP2Fm3zOzJ83sgJn9iZn9ebjNNjNbEG73JjP7gZltN7MfmdmyYr5+Ofckxt5EJJIuJzh1+jXgx8D7zOw5gkX7f8fdnzezOUAXcBPB2km/EAbhJ8zs0nA/bwv3VUZwtt2n3P1yM/sX4A8IVqpcD/yRu+81s/cA/0awRo7ItFAgl9nqOXc/AmBmLxKsm3EaOObuzwN4uNKlma0AvhDmNZjZQYK1NACecvc2oM3MTgOPhPkvAb8Yrpz5K8ADOQvhlU7xaxMZRIFcZquenPtpJv5ez91PJiedCfcZI1hb+h0T3L/IpKmPXM4lu4EL+i8IEPaPJwhWdPxYmHcp8MZw2zGFrfr9ZvbR8PlmZm+fisKLjESBXM4Z7t4L/A7wBTPbSXDR3TKCPu2Ymb1E0If+CXfvGXlPZ/kYcF24z10EF7kQmTZa/VBEJOLUIhcRiTgFchGRiFMgFxGJOAVyEZGIUyAXEYk4BXIRkYhTIBcRibj/D5U6MXMfDVGIAAAAAElFTkSuQmCC\n" - }, - "metadata": { - "needs_background": "light" - } - }, - { - "output_type": "stream", - "name": "stdout", - "text": [ - "---------------------------\n", - "\n", - "Resultados do Polinomial de Grau: 8\n", - "\n", - "Resultado do conjunto de treino - Grau 8 :\n", - "As variáveis explicativas do meu modelo explicam -19.65 % das variações na renda dos clientes.\n", - "O erro médio absoluto do modelo é: 13498.63\n", - "O erro médio quadrático do modelo é: 556149206.51\n", - "A raiz quadrada do erro médio quadrático é: 12704.330739940186\n", - "Acurácia: 0.75\n", - "\n", - "Veja o comportamento dos resíduos:\n" - ] - }, - { - "output_type": "display_data", - "data": { - "text/plain": [ - "
" - ], - "image/png": 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\n" - }, - "metadata": { - "needs_background": "light" - } - }, - { - "output_type": "stream", - "name": "stdout", - "text": [ - "\n", - "Resultado do conjunto de teste - Grau 8 :\n", - "As variáveis explicativas do meu modelo explicam -619623100123.76 % das variações na renda dos clientes.\n", - "O erro médio absoluto do modelo é: 335314643.48\n", - "O erro médio quadrático do modelo é: 2.8637397605052314e+18\n", - "A raiz quadrada do erro médio quadrático é: 12704.330739940186\n", - "Acurácia: 0.65\n", - "\n", - "Veja o comportamento dos resíduos:\n" - ] - }, - { - "output_type": "display_data", - "data": { - "text/plain": [ - "
" - ], - "image/png": 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\n" - }, - "metadata": { - "needs_background": "light" - } - }, - { - "output_type": "stream", - "name": "stdout", - "text": [ - "---------------------------\n", - "\n", - "Resultados do Polinomial de Grau: 9\n", - "\n", - "Resultado do conjunto de treino - Grau 9 :\n", - "As variáveis explicativas do meu modelo explicam -123.35 % das variações na renda dos clientes.\n", - "O erro médio absoluto do modelo é: 18145.33\n", - "O erro médio quadrático do modelo é: 1038203340.48\n", - "A raiz quadrada do erro médio quadrático é: 12704.330739940186\n", - "Acurácia: 0.75\n", - "\n", - "Veja o comportamento dos resíduos:\n" - ] - }, - { - "output_type": "display_data", - "data": { - "text/plain": [ - "
" - ], - "image/png": 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\n" - }, - "metadata": { - "needs_background": "light" - } - }, - { - "output_type": "stream", - "name": "stdout", - "text": [ - "\n", - "Resultado do conjunto de teste - Grau 9 :\n", - "As variáveis explicativas do meu modelo explicam -973903563350.57 % das variações na renda dos clientes.\n", - "O erro médio absoluto do modelo é: 346495165.35\n", - "O erro médio quadrático do modelo é: 4.5011336029014753e+18\n", - "A raiz quadrada do erro médio quadrático é: 12704.330739940186\n", - "Acurácia: 0.65\n", - "\n", - "Veja o comportamento dos resíduos:\n" - ] - }, - { - "output_type": "display_data", - "data": { - "text/plain": [ - "
" - ], - "image/png": 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j1n4AgHjHQRY03Ea84yAAifYDQbnz9aB87NWgfOJwUG7bx4KG24h1HQUgeeTloNwTjKMmD7/EgobbsN5guKCsdVdQ7gsOXpW1/CIop3uC8qEXWdBwG2T6ACg/uCMoZ4NeX/lrzwblUPmBBuY/8+1cuWL/U8x/9o6B8quPU/Pcnbly5Ss/o2bHXQPlfY9R88LdA+WmrVS/+INcuerlLVTvvHegvOcRqn9530B590PMa3wgV56368fM2/XjgXLjA1TtfihXrv7lfVTteWSgvPNeql7eMlB+8QdUNm3NlWteuJvKfY8NlHfcReUrPxsoP3cnFa8+nivPf/YOKvY/NVB+5tuUHxg4HcSChtsof+3ZoJDNBOWDO4Jypo8FDbdRduhFACzdE5Rbgj6J9Z0Iyq27gnJvR/D4Iy8BUNbXwfztA8+9eNdRahq+RezovqB84jCVT/4jZe2vBrs7dpAjP/kGfYdfpTuVYXHmKPX19ezaFWz/g2emqX76W3j7ITLZLBzZR+VT/0h1XxtrVy9n+/bt1NfX09zcDMCTTz5JfX09hw4dAuDnP/859fX1HD4cPFe3bdtGfX09x44dA2DLli3U19fT2RkMRz388MPU19fT0xM8Fx988EHq6+tJp4Nxtfvvv5/6+vpcW957771cccUVufLdd9/NlVdemStv3ryZq666Kle+4447uPrqq3Pl22+/nWuuuSZX3rRpE9dee22ufOutt3L99dfnyrfccgtf/vKXc+Ubb7yR9evX58o33HADN9xwQ668fv16brzxxlz5y1/+MrfcckuufP3113Prrbfmytdeey2bNm3Kla+55hpuv/32XPnqq6/mjjsGXltXXXUVmzdvBoKgXvzinVy5/Aib69/LmhV1XHHFFdx778Br586//Uv+2+mtPPb5C7jj06u482//ctTcq6+vZ8uW4LVx7NixQW0/VDGC/Exgf165OVw2iJnVm1mDmTWkUqmhd4/qYHs3idjAl0qCqV7BeGoMy/VWRpLJBj2ajp50MI945o90SESYQUUiRszAcQxyBybh5GEWd4jbwMvOgZ5UcCHo//KuwS+bdy2rZfniat50ahVmBmZUJOP8+Qd0Yi4ZrOCTZpnZx4CL3P3ysPzfgfe4+5+M9JiJnjTr/PVbWFiZDJ7MQOOh48QMetNORSKGmZ10XuihyhOW+wicyijJpXDJGKxadiprVy/n+eZjbPrpXk70ZZhXFicZM06kMmSyPuj51j+FMZ3NUpaIsai6vOgfs2X2GumkWcU42HkAWJpXXhIuK5qhR3z7v0kXCw8sjdUjh2Acs38OcDqTUad8lorZkDHrAhhBj7ksbpy2oJJ1F68cMWzXrKjjygvPyZXPX7+F0xdWYmZ09KTYf7QrN05uMaitLNMUQimaYgytPA2cbWZnmVkZ8AngvjEeMyFrVy8nlXG6+tK4O/MrE2Q9OJCZxUlnRz9MWV0eJ+tQU5FgUXXZoI++MjHzymK88ZQqbr/03SypreTcM+bzq2cuoC6vXc3gcxeezb6vfDj3c/ul7+a85acyvyJBPGYYUJmMsbS2kkXzkpgFB/EqEjGSQ56V8Vgwl7wiYcwrC85C2N+zrUjGgi9aJGMkYsbKMxYwryxe1P/jeMxGDfHh5F89qKYiydJTqihPxChPxHjH0lqFuBRVwT1yd0+b2Z8ADxFMP/yWu+8suGZ5hh7xXXZqNZe8O5iF8tLrx+nLOAsqxzdrpbmti7Prqqd91sp5Z9Wyee37JvSY/pk6U3GUe7yGOxd3/1S5/k9KmaxzrDtNMh7D3YnFjHueOcDbliw8qb7zK5OsPGPBqOcLb2rtJBZ2XxNxY/niarr60rR29LK4pjz4f8sE+8m6UxaPMb8ywdETKbr60iyqLuPAsR7cfdyfvMriRl/GScYg7YBDVVmcRdVlZH3iX6MeOt0sHjPq5ldM+A1BZDx0YQkZ1dDjEwDuTnt3ir/+6LnBKWqP95B1xzCyOGcsqCQRN+pqKthc/95B55nIn0ObH2r567xy5AQxC85rcsaCSuZXJnF3Dh0Pgr4vneHIib6wMrCopoxkPM7H3nlm7s0agoPk6XG8OydilpufHXxaMM5YWEFNRZKuvnTu75iomfBGLLPLVI6Ryyw22jfS+j8prf3udpzgK/KLqitywdsfqOM5z8TgT13dmMEbaipy86y7U5nclXc2bGsinQk+iZUlYiw7tToXkv2T3y7Z+ARliViu957KZnMzSPK7LuWJGLVVSdq6UlSUxejLOKfMS1JdHtSxkC9trFlRp+CWaaEgl1GN9Y20NSvqeOcba0f9+vF4zzPRH3z9vfNE3HD3Qfscbzj277P/zIgxM9JhkifjRnV5nK6+LDXlcc5aVM1Xwm2rFy1RpCCXUY3nG2ljhf1EzzNRjG/B9e8z/8yI2VQGM2NBRYKzR7jgsnrREkUaI5eiGK0nO54x8qmoz3TvU2SqjTRGriCXaVGKIQsNk8hsoyAXEYm4kYJcF18WEYk4BbmISMQpyEVEIk7TD2VaFHKhZREZnXrkMuX6pwK2dPQMukbl1sbhL8wsIhOjIJcpl/8VfbPgdzJubNjWVOqqicwKCnKZcvvbuqhMxgctG+4r+iIyOQpymXL55+buN9pX9EVkYhTkMuWGXhik0LMKishgmrUiU64YJ8Hqp9kvIidTkMu0KMZZBfNPhJU/+2UdE7+Cj8hsoqEViQzNfhEZnoJcIkOzX0SGpyCXyNDsF5HhKcglMjT7RWR4CnKJjDUr6lh38Urqaipo705RV1OhK/6IoFkrEjG6pqbIydQjFxGJOAW5iEjEKchFRCJOQS4iEnEKchGRiFOQi4hEnIJcRCTiFOQiIhGnIBcRiTgFuYhIxCnIRUQiTkEuIhJxCnIRkYhTkIuIRJyCXEQk4hTkIiIRV1CQm9nHzWynmWXNbFWxKiUiIuNXaI/8ReD3gG1FqIuIiExCQZd6c/dfAphZcWojIiITNm1j5GZWb2YNZtbQ2to6XbsVEZn1xuyRm9mjwGnD3PVFd//ReHfk7huBjQCrVq3ycddQRERGNWaQu/uF01ERERGZHE0/FBGJuEKnH/6umTUD5wH/YmYPFadaIiIyXoXOWrkXuLdIdRERkUnQ0IqISMQpyEVEIq6goRWRuejmR3ez6ad7OdGXYV5ZnMvPP4srLzyn1NWSOUxBLjIBNz+6m5u27CFmkIhBdyrDTVv2AIwZ5lsbW9iwrYn9bV0sra1i7erlrFlRNx3VlllOQysiE7Dpp3vDEI8Rs1j4O1g+mq2NLVx3305aOnpYWJmkpaOH6+7bydbGlmmqucxmCnKRCTjRlyE25NRCMQuWj2bDtiaScaOqLIFZ8DsZNzZsa5rC2spcoSAXmYB5ZXGyQ04wkfVg+Wj2t3VRmRy8TmUyTnNbV7GrKHOQglxkAi4//yyyDulslqxnw9/B8tEsra2iOzW4196dyrCktmoqqytzhIJcZAKuvPAcPnvBW6hMxklng171Zy94y5gHOteuXk4q43T1pXEPfqcyztrVy6ep5jKbmfv0n4hw1apV3tDQMO37lblnJs0U6a9Lc1sXSzRrRSbBzLa7+0lXY9P0Q5m1+meKJOM2aKbIOihJgK5ZUafglimhoRWZtTRTROYKBbnMWpopInOFglxmLc0UkblCQS6zlmaKyFyhIJdZa82KOtZdvJK6mgrau1PU1VSw7uKVOuAos45mrcisppkiMheoRy4iEnEKchGRiFOQi4hEnIJcRCTiFOQiIhGnIBcRiTgFuYhIxCnIRUQiTkEuIhJxCnIRkYhTkIuIRJyCXEQk4nTSLJn1ZtJ1O0WmgnrkMqv1X7ezpaNn0HU7tza2lLpqIkWjIJdZTdftlLlAQS6zmq7bKXOBglxmNV23U+YCBbnMarpup8wFCnKZ1XTdTpkLNP1QZj1dt1NmO/XIRUQiTkEuIhJxBQW5mX3NzBrN7Hkzu9fMFharYiIiMj6F9sgfAc5197cBu4FrCq+SiIhMREFB7u4Pu3s6LD4BLCm8SiIiMhHFHCP/NPDjke40s3ozazCzhtbW1iLuVkRkbhtz+qGZPQqcNsxdX3T3H4XrfBFIA98baTvuvhHYCLBq1SqfVG1FROQkYwa5u1842v1mdinwEeA/ubsCWkRkmhX0hSAzuwj4c+A33V1nIRIRKYFCx8hvAWqAR8zsOTO7tQh1EhGRCSioR+7ubylWRUREZHL0zU4RkYhTkIuIRJyCXEQk4hTkIiIRpyAXEYk4BbmISMQpyEVEIk5BLiIScQpyEZGIU5CLiEScglxEJOIU5CIiEacgFxGJOAW5iEjEKchFRCJOQS4iEnEKchGRiFOQi4hEnIJcRCTiFOQiIhGnIBcRiTgFuYhIxCnIRUQiTkEuIhJxCnIRkYhLlLoCIqVw86O72fTTvZzoyzCvLM7l55/FlReeU+pqiUyKglzmnJsf3c1NW/YQM0jEoDuV4aYtewAU5hJJGlqROWfTT/eGIR4jZrHwd7BcJIoU5DLnnOjLELPBy2IWLBeJIgW5zDnzyuJkffCyrAfLRaJIQS5zzuXnn0XWIZ3NkvVs+DtYLhJFOtgpc07/AU3NWpHZwtx97LWKbNWqVd7Q0DDt+xURiTIz2+7uq4Yu19CKiEjEKchFRCJOQS4iEnEKchGRiFOQi4hEXElmrZhZK/DKOFdfBByewupMluo1MarXxKheEzNX6vUmd188dGFJgnwizKxhuOk2paZ6TYzqNTGq18TM9XppaEVEJOIU5CIiEReFIN9Y6gqMQPWaGNVrYlSviZnT9ZrxY+QiIjK6KPTIRURkFApyEZGoc/cZ+QNcBOwC9gB/MUX7WAr8G/ALYCfw2XD5KcAjwEvh79pwuQE3h3V6Hnhn3rY+Fa7/EvCpvOXvAl4IH3Mz4XDWOOoWB54FHgjLZwFPhtv5J6AsXF4elveE9y/L28Y14fJdwAeK0bbAQuAeoBH4JXDeDGmvq8L/wxeBzUBFKdoM+BbQAryYt2zK22ekfYxRr6+F/4/PA/cCCyfbDpNp65HqlXff/wYcWDQT2itc/qdhm+0Evjrd7TXi826i4TcdPwQh9jKwHCgDdgC/MgX7Ob3/yQDUALuBXwG+2t/owF8A68PbHwJ+HD6h3gs8mfekaAp/14a3+1+sT4XrWvjYD46zbp8D7mQgyL8PfCK8fSvwx+HtK4Bbw9ufAP4pvP0rYbuVh0+al8N2LahtgW8Dl4e3ywiCvaTtBZwJ7AUq89rq0lK0GbAaeCeDA3PK22ekfYxRr98GEuHt9Xn1mnA7TLStR6tXuHwp8BDBFwcXzZD2+i3gUaA8LNdNd3uN+BoodjgW44egl/dQXvka4Jpp2O+PgPcTvIOeHi47HdgV3t4AXJK3/q7w/kuADXnLN4TLTgca85YPWm+UeiwB/hW4AHggfBIeZuBFl2uf8Ml+Xng7Ea5nQ9usf71C2hZYQBCYNmR5qdvrTGA/wQs5EbbZB0rVZsAyBgfAlLfPSPsYrV5D7vtd4HvD/X1jtcNknp9j1YvgU9/bgX0MBHlJ24sgfC8cZr1pba/hfmbqGHn/C7Nfc7hsypjZMuAdBB9n3uDuB8O7DgFvGKNeoy1vHmb5WG4E/hzIhuVTgWPunh5mO7l9h/e3h+tPtK7jcRbQCtxmZs+a2SYzm0eJ28vdDwBfB14FDhK0wXZmRpvB9LTPSPsYr08T9FgnU6/JPD9HZGYfBQ64+44hd5W6vc4B/qOZPWlm/25m755kvYraXqCDnQCYWTXwA+B/ufvx/Ps8eGv0aazLR4AWd98+XfucgATBx83/5+7vAE4QfCzNme72AjCzWuCjBG80ZwDzCMYmZ5zpaJ+J7sPMvgikge9NWaXGX5cq4AvAddO1zwm0V4LgU997gauB75uZTWXdxmumBvkBgjGyfkvCZUVnZkmCEP+eu/8wXPy6mZ0e3n86wUGP0eo12vIlwywfzW8AF5vZPuAuguGVm4CFZtZ/jdX87eT2Hd6/ADgyibqORzPQ7O5PhuV7CIK9lO0FcCGw191b3T0F/JCgHWdCm8H0tM9I+xiVmV0KfAT4ZBhok6nXESbe1iN5M8Eb8o7wNbAEeMbMTptEvYrdXs3ADz3wFMEn5kWTqFcx22hT/qAAAANMSURBVCsw1thLKX4I3vmaCP5D+w8SrJyC/RjwHeDGIcu/xuADIV8Nb3+YwQdbngqXn0Iwdlwb/uwFTgnvG3qw5UMTqN8aBg523s3ggyNXhLf/J4MPjnw/vL2SwQdgmggOvhTUtsBjwFvD238VtlVJ2wt4D8Esgqrwcd8mmF1Qkjbj5LHVKW+fkfYxRr0uIpixtXjIehNuh4m29Wj1GnLfPgbGyEvdXp8B1oW3zyEYArHpbq9h26lYoVjsH4Ij1LsJjvp+cYr2cT7BR6rngefCnw8RjEn9K8HUpEfznhQG/H1YpxeAVXnb+jTBlKE9wB/lLV9FMCXuZeAWxjmdLnzsGgaCfHn4pNwTPgn6j5xXhOU94f3L8x7/xXC/u8ib/VFI2wK/BjSEbfbP4Qun5O0FXE8wLexF4I7wRTXtbUYw9fEgkCLowV02He0z0j7GqNcegjDqf+7fOtl2mExbj1SvIffvY/D0w1K2Vxnw3XB7zwAXTHd7jfSjr+iLiETcTB0jFxGRcVKQi4hEnIJcRCTiFOQiIhGnIBcRiTgFucwaZtZZ6jqIlIKCXEQk4hTkMuuY2Roz22pm95hZo5l9r/+cGGb2bjP7uZntMLOnzKzGzCrM7DYzeyE8Gdhvheteamb/bGaPmNk+M/sTM/tcuM4TZnZKuN6bzewnZrbdzB4zsxWl/Ptl7kmMvYpIJL2D4KvTrwE/A37DzJ4iOGn/77v702Y2H+gGPktw7qRfDUP4YTM7J9zOueG2Kgi+bfd5d3+Hmf0d8IcEZ6rcCHzG3V8ys/cA3yQ4R47ItFCQy2z1lLs3A5jZcwTnzWgHDrr70wAenunSzM4HvhEuazSzVwjOpQHwb+7eAXSYWTtwf7j8BeBt4Zkz3wfcnXcivPIp/ttEBlGQy2zVm3c7w+Sf6/nbyeaVs+E2YwTnlv61SW5fpGAaI5e5ZBdwev8FAcLx8QTBGR0/GS47B3hjuO6Ywl79XjP7ePh4M7O3T0XlRUaiIJc5w937gN8HvmFmOwguultBMKYdM7MXCMbQL3X33pG3dJJPApeF29xJcJELkWmjsx+KiESceuQiIhGnIBcRiTgFuYhIxCnIRUQiTkEuIhJxCnIRkYhTkIuIRNz/B9++Jsa7+fGYAAAAAElFTkSuQmCC\n" - }, - "metadata": { - "needs_background": "light" - } - }, - { - "output_type": "stream", - "name": "stdout", - "text": [ - "---------------------------\n", - "\n" - ] - } - ] - }, - { - "cell_type": "markdown", - "source": [ - "**Salvando o melhor modelo com o pickle** --> TIRAR DÚVIDA NA AULA SOBRE COMO SALVAR O MELHOR MODELO COM POLINOMIAL " - ], - "metadata": { - "id": "7JK6t95c-c5t" - } - }, - { - "cell_type": "markdown", - "source": [ - "\n", - "ERRADO\n", - "```\n", - "import pickle\n", - "with open ('Reg.pkl','wb') as modelo:\n", - " pickle.dump(pf,modelo)\n", - "\n", - " with open ('Reg.pkl','rb') as modelo:\n", - " regressao=pickle.load(modelo)\n", - "```\n", - "\n" - ], - "metadata": { - "id": "Jax6x5eZoQu1" - } - }, - { - "cell_type": "markdown", - "source": [ - "###### **Substituindo os valores**" - ], - "metadata": { - "id": "mDWrFgTGA8Kr" - } - }, - { - "cell_type": "markdown", - "source": [ - "Agora que os salários foram estimados, vamos substituir os nulos da variável \"income\" na tabela df_2 pelos valores estimados no melhor modelo (polinomial de grau 2)." - ], - "metadata": { - "id": "KrNOCvqK4rnA" - } - }, - { - "cell_type": "code", - "source": [ - "df_5=df_2[df_2['Income'].isnull()]\n", - "X_incnulo=df_5[['Kidhome',\n", - "'MntWines',\n", - "'MntFruits',\n", - "'MntFishProducts', \n", - "'MntSweetProducts',\n", - "'NumCatalogPurchases',\n", - "'NumStorePurchases',\n", - "'NumWebVisitsMonth']]" - ], - "metadata": { - "id": "GtY3xpmMnO76" - }, - "execution_count": 124, - "outputs": [] - }, - { - "cell_type": "code", - "source": [ - "Pipeline_Regressao(X_incnulo, df_5['Income'], 2)" - ], - "metadata": { - "id": "-YKxI8k5__zW", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 937 - }, - "outputId": "f14d839d-85fa-4123-f0f5-d5425ef63206" - }, - "execution_count": 125, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Resultados do Polinomial de Grau: 2\n", - "\n", - "Resultado do conjunto de treino - Grau 2 :\n", - "As variáveis explicativas do meu modelo explicam 81.69 % das variações na renda dos clientes.\n", - "O erro médio absoluto do modelo é: 6953.15\n", - "O erro médio quadrático do modelo é: 85098902.1\n", - "A raiz quadrada do erro médio quadrático é: 12704.330739940186\n", - "Acurácia: 0.75\n", - "\n", - "Veja o comportamento dos resíduos:\n" - ] - }, - { - "output_type": "display_data", - "data": { - "text/plain": [ - "
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\n" - }, - "metadata": { - "needs_background": "light" - } - }, - { - "output_type": "stream", - "name": "stdout", - "text": [ - "\n", - "Resultado do conjunto de teste - Grau 2 :\n", - "As variáveis explicativas do meu modelo explicam 65.3 % das variações na renda dos clientes.\n", - "O erro médio absoluto do modelo é: 8150.09\n", - "O erro médio quadrático do modelo é: 160362399.84\n", - "A raiz quadrada do erro médio quadrático é: 12704.330739940186\n", - "Acurácia: 0.65\n", - "\n", - "Veja o comportamento dos resíduos:\n" - ] - }, - { - "output_type": "display_data", - "data": { - "text/plain": [ - "
" - ], - "image/png": 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https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", - " self[col] = igetitem(value, i)\n" - ] - } - ] - }, - { - "cell_type": "code", - "source": [ - "df_5.head()" - ], - "metadata": { - "id": "48d7KVXiT-Xs", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 299 - }, - "outputId": "a705fcb9-e780-4dce-d8f7-17076390ee1f" - }, - "execution_count": 128, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - " ID Year_Birth Education Marital_Status Income Kidhome Teenhome \\\n", - "10 1994 1983.0 Graduation Married NaN 1 0 \n", - "27 5255 1986.0 Graduation Single NaN 1 0 \n", - "43 7281 1959.0 PhD Single NaN 0 0 \n", - "48 7244 1951.0 Graduation Single NaN 2 1 \n", - "58 8557 1982.0 Graduation Single NaN 1 0 \n", - "\n", - " Dt_Customer Recency MntWines ... 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\n", - " " - ] - }, - "metadata": {}, - "execution_count": 128 - } - ] - }, - { - "cell_type": "code", - "source": [ - "df_2['Income'].fillna(df_5['Income_pred'],inplace=True)" - ], - "metadata": { - "id": "GJueZdCGUVAK" - }, - "execution_count": 129, - "outputs": [] - }, - { - "cell_type": "code", - "source": [ - "df_2.isnull().sum()" - ], - "metadata": { - "id": "n9skr02PYnt2", - "outputId": "554bac38-620b-4d63-de6d-ce40ad2dc892", - "colab": { - "base_uri": "https://localhost:8080/" - } - }, - "execution_count": 130, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "ID 0\n", - "Year_Birth 0\n", - "Education 0\n", - "Marital_Status 0\n", - "Income 0\n", - "Kidhome 0\n", - "Teenhome 0\n", - "Dt_Customer 0\n", - "Recency 0\n", - "MntWines 0\n", - "MntFruits 0\n", - "MntMeatProducts 0\n", - "MntFishProducts 0\n", - "MntSweetProducts 0\n", - "MntGoldProds 0\n", - "NumDealsPurchases 0\n", - "NumWebPurchases 0\n", - "NumCatalogPurchases 0\n", - "NumStorePurchases 0\n", - "NumWebVisitsMonth 0\n", - "AcceptedCmp3 0\n", - "AcceptedCmp4 0\n", - "AcceptedCmp5 0\n", - "AcceptedCmp1 0\n", - "AcceptedCmp2 0\n", - "Complain 0\n", - "Response 0\n", - "Ec_Divorced 0\n", - "Ec_Married 0\n", - "Ec_Single 0\n", - "Ec_Together 0\n", - "Ec_Widow 0\n", - "Ed_Basic 0\n", - "Ed_Graduation 0\n", - "Ed_Master 0\n", - "Ed_PhD 0\n", - "dtype: int64" - ] - }, - "metadata": {}, - "execution_count": 130 - } - ] - }, - { - "cell_type": "code", - "source": [ - "## Com isso, finalizamos a parte de tratamento dos dados (FINALMENTE!)\n", - "## Agora, vamos fazer as análises gráficas e etc p/ ajudar a equipe de marketing na próxima campanha." - ], - "metadata": { - "id": "jBKbiGyleaK9" - }, - "execution_count": 131, - "outputs": [] - }, - { - "cell_type": "markdown", - "source": [ - "### **Analisando os dados**" - ], - "metadata": { - "id": "2zIj9La0fUpJ" - } - }, - { - "cell_type": "code", - "source": [ - "## Em primeiro lugar, vamos fazer duas coisas. Relembrar nosso objetivo e visualizar o dataframe \"df\", em que substituimos os dados da renda manualmente. " - ], - "metadata": { - "id": "MkXKBGbSfqn2" - }, - "execution_count": 132, - "outputs": [] - }, - { - "cell_type": "markdown", - "source": [ - "**The Objective**\n", - "\n", - "The objective of the team is to build an analysis to address the highest profit for the next direct marketing campaign, scheduled for the next month. The new campaign, sixth, aims at selling a new gadget to the Customer Database. To build the analysis, a pilot campaign involving 2.240 customers was carried out. The customers were selected at random and contacted by phone regarding the acquisition of the gadget. During the following months, customers who bought the\n", - "offer were properly labeled. The total cost of the sample campaign was 6.720MU and the revenue generated by the customers who accepted the offer was 3.674MU. Globally the campaign had a profit of -3.046MU. The success rate of the campaign was 15%." - ], - "metadata": { - "id": "VR4lkPhRf3Ld" - } - }, - { - "cell_type": "code", - "source": [ - "## Revendo o datafreme\n", - "df.info()" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "IoZGO137gKdK", - "outputId": "7848de62-a4f4-4dcc-f01d-ea446bc052ed" - }, - "execution_count": 133, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "\n", - "Int64Index: 2233 entries, 0 to 2239\n", - "Data columns (total 27 columns):\n", - " # Column Non-Null Count Dtype \n", - "--- ------ -------------- ----- \n", - " 0 ID 2233 non-null int64 \n", - " 1 Year_Birth 2233 non-null float64\n", - " 2 Education 2233 non-null object \n", - " 3 Marital_Status 2233 non-null object \n", - " 4 Income 2233 non-null float64\n", - " 5 Kidhome 2233 non-null int64 \n", - " 6 Teenhome 2233 non-null int64 \n", - " 7 Dt_Customer 2233 non-null object \n", - " 8 Recency 2233 non-null int64 \n", - " 9 MntWines 2233 non-null int64 \n", - " 10 MntFruits 2233 non-null int64 \n", - " 11 MntMeatProducts 2233 non-null int64 \n", - " 12 MntFishProducts 2233 non-null int64 \n", - " 13 MntSweetProducts 2233 non-null int64 \n", - " 14 MntGoldProds 2233 non-null int64 \n", - " 15 NumDealsPurchases 2233 non-null int64 \n", - " 16 NumWebPurchases 2233 non-null int64 \n", - " 17 NumCatalogPurchases 2233 non-null int64 \n", - " 18 NumStorePurchases 2233 non-null int64 \n", - " 19 NumWebVisitsMonth 2233 non-null int64 \n", - " 20 AcceptedCmp3 2233 non-null int64 \n", - " 21 AcceptedCmp4 2233 non-null int64 \n", - " 22 AcceptedCmp5 2233 non-null int64 \n", - " 23 AcceptedCmp1 2233 non-null int64 \n", - " 24 AcceptedCmp2 2233 non-null int64 \n", - " 25 Complain 2233 non-null int64 \n", - " 26 Response 2233 non-null int64 \n", - "dtypes: float64(2), int64(22), object(3)\n", - "memory usage: 488.5+ KB\n" - ] - } - ] - }, - { - "cell_type": "code", - "source": [ - "## Vamos criar uma variável de \"idade\" e outra de \"tempo de cliente\" para compor nossa análise\n", - "df['Dt_Customer']=pd.to_datetime(df['Dt_Customer'], format='%Y-%m-%d')" - ], - "metadata": { - "id": "HDlcocOkH3d1" - }, - "execution_count": 134, - "outputs": [] - }, - { - "cell_type": "code", - "source": [ - "df" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 488 - }, - "id": "exwEjbHqKYH0", - "outputId": "aceb7834-164c-4745-adfb-3dd9be08e481" - }, - "execution_count": 135, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - " ID Year_Birth Education Marital_Status Income Kidhome \\\n", - "0 5524 1957.0 Graduation Single 58138.0 0 \n", - "1 2174 1954.0 Graduation Single 46344.0 1 \n", - "2 4141 1965.0 Graduation Together 71613.0 0 \n", - "3 6182 1984.0 Graduation Together 26646.0 1 \n", - "4 5324 1981.0 PhD Married 58293.0 1 \n", - "... ... ... ... ... ... ... \n", - "2235 10870 1967.0 Graduation Married 61223.0 0 \n", - "2236 4001 1946.0 PhD Together 64014.0 2 \n", - "2237 7270 1981.0 Graduation Divorced 56981.0 0 \n", - "2238 8235 1956.0 Master Together 69245.0 0 \n", - "2239 9405 1954.0 PhD Married 52869.0 1 \n", - "\n", - " Teenhome Dt_Customer Recency MntWines ... 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055241957.0GraduationSingle58138.0002012-09-0458635...10470000001
121741954.0GraduationSingle46344.0112014-03-083811...1250000000
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361821984.0GraduationTogether26646.0102014-02-102611...0460000000
453241981.0PhDMarried58293.0102014-01-1994173...3650000000
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\n", - " " - ] - }, - "metadata": {}, - "execution_count": 135 - } - ] - }, - { - "cell_type": "code", - "source": [ - "## Para criar a variável referente à idade, precisamos saber de qual ano é a base de dados.\n", - "df['Dt_Customer'].max()" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "6Dz2ZT0lFNq9", - "outputId": "5a6f7de8-dad8-4ede-dffa-fe8c72339dab" - }, - "execution_count": 136, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "Timestamp('2014-06-29 00:00:00')" - ] - }, - "metadata": {}, - "execution_count": 136 - } - ] - }, - { - "cell_type": "code", - "source": [ - "## Parece que a base é de 2014, então a idade dos clientes deve ser o resultado da seguinte fórmula:\n", - "df['Age']=2014-df['Year_Birth']" - ], - "metadata": { - "id": "jsjEQktRE3NT" - }, - "execution_count": 137, - "outputs": [] - }, - { - "cell_type": "code", - "source": [ - "## Já pra ver a quanto tempo cada ID é um cliente da empresa, a fórmula é:\n", - "df['Cust_for']=(df['Dt_Customer'].max()-df['Dt_Customer']).dt.days" - ], - "metadata": { - "id": "znTS61GPY0L_" - }, - "execution_count": 138, - "outputs": [] - }, - { - "cell_type": "code", - "source": [ - "## Agora, vamos criar outras três variáveis. Uma do total gasto nos últimos dois anos, outra do total gasto em produtos regulares (não ouro)\n", - "## e outra da quantidade total de compras no período.\n", - "df['MntTotal']=(df['MntFishProducts']+df['MntMeatProducts']+df['MntSweetProducts']+df['MntWines']+df['MntFruits'])\n", - "df['MntRegProds']=(df['MntTotal']-df['MntGoldProds'])\n", - "df['NumPurchases']=(df['NumWebPurchases']+df['NumCatalogPurchases']+df['NumStorePurchases'])" - ], - "metadata": { - "id": "xWMiyCeBxXXz" - }, - "execution_count": 139, - "outputs": [] - }, - { - "cell_type": "code", - "source": [ - "## Vou mover as novas colunas para outras posições no df.\n", - "df.columns" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "DAZUlLhpGMvD", - "outputId": "00cb035d-5ae3-4b1b-81f8-ada0697e1b1b" - }, - "execution_count": 140, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "Index(['ID', 'Year_Birth', 'Education', 'Marital_Status', 'Income', 'Kidhome',\n", - " 'Teenhome', 'Dt_Customer', 'Recency', 'MntWines', 'MntFruits',\n", - " 'MntMeatProducts', 'MntFishProducts', 'MntSweetProducts',\n", - " 'MntGoldProds', 'NumDealsPurchases', 'NumWebPurchases',\n", - " 'NumCatalogPurchases', 'NumStorePurchases', 'NumWebVisitsMonth',\n", - " 'AcceptedCmp3', 'AcceptedCmp4', 'AcceptedCmp5', 'AcceptedCmp1',\n", - " 'AcceptedCmp2', 'Complain', 'Response', 'Age', 'Cust_for', 'MntTotal',\n", - " 'MntRegProds', 'NumPurchases'],\n", - " dtype='object')" - ] - }, - "metadata": {}, - "execution_count": 140 - } - ] - }, - { - "cell_type": "code", - "source": [ - "df=df[['ID', 'Year_Birth','Age','Cust_for', 'Education', 'Marital_Status', 'Income', 'Kidhome',\n", - " 'Teenhome', 'Dt_Customer', 'Recency', 'MntTotal', 'MntWines', 'MntFruits',\n", - " 'MntMeatProducts', 'MntFishProducts', 'MntSweetProducts',\n", - " 'MntGoldProds','MntRegProds','NumPurchases', 'NumDealsPurchases', 'NumWebPurchases',\n", - " 'NumCatalogPurchases', 'NumStorePurchases', 'NumWebVisitsMonth',\n", - " 'AcceptedCmp3', 'AcceptedCmp4', 'AcceptedCmp5', 'AcceptedCmp1',\n", - " 'AcceptedCmp2', 'Complain', 'Response']]" - ], - "metadata": { - "id": "Grh9RUXEBNbV" - }, - "execution_count": 141, - "outputs": [] - }, - { - "cell_type": "code", - "source": [ - "df.head()" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 299 - }, - "id": "C3vuSa4rzR-t", - "outputId": "c7a3ec46-5bff-4bc4-e20e-76886a627d1d" - }, - "execution_count": 142, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - " ID Year_Birth Age Cust_for Education Marital_Status Income \\\n", - "0 5524 1957.0 57.0 663 Graduation Single 58138.0 \n", - "1 2174 1954.0 60.0 113 Graduation Single 46344.0 \n", - "2 4141 1965.0 49.0 312 Graduation Together 71613.0 \n", - "3 6182 1984.0 30.0 139 Graduation Together 26646.0 \n", - "4 5324 1981.0 33.0 161 PhD Married 58293.0 \n", - "\n", - " Kidhome Teenhome Dt_Customer ... NumCatalogPurchases NumStorePurchases \\\n", - "0 0 0 2012-09-04 ... 10 4 \n", - "1 1 1 2014-03-08 ... 1 2 \n", - "2 0 0 2013-08-21 ... 2 10 \n", - "3 1 0 2014-02-10 ... 0 4 \n", - "4 1 0 2014-01-19 ... 3 6 \n", - "\n", - " NumWebVisitsMonth AcceptedCmp3 AcceptedCmp4 AcceptedCmp5 AcceptedCmp1 \\\n", - "0 7 0 0 0 0 \n", - "1 5 0 0 0 0 \n", - "2 4 0 0 0 0 \n", - "3 6 0 0 0 0 \n", - "4 5 0 0 0 0 \n", - "\n", - " AcceptedCmp2 Complain Response \n", - "0 0 0 1 \n", - "1 0 0 0 \n", - "2 0 0 0 \n", - "3 0 0 0 \n", - "4 0 0 0 \n", - "\n", - "[5 rows x 32 columns]" - ], - "text/html": [ - "\n", - "
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\n", - " " - ] - }, - "metadata": {}, - "execution_count": 142 - } - ] - }, - { - "cell_type": "code", - "source": [ - "## Feito esses ajustes, vamos analisar os dados.\n", - "## O objetivo é analisar dados de uma campanha prévia para indicar caminhos para uma campanha futura,\n", - "## portanto, vou separar os dados em dois dataframes, um com os clientes que compraram o produto e outro com os que não compraram.\n", - "\n", - "df_r1=df[df['Response']==1]\n", - "df_r0=df[df['Response']==0]" - ], - "metadata": { - "id": "rZppGv7qgN-l" - }, - "execution_count": 143, - "outputs": [] - }, - { - "cell_type": "code", - "source": [ - "df_r1" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 488 - }, - "id": "MKm2enlohSOM", - "outputId": "18915da1-e3e3-467a-a8ae-f124bd378fa2" - }, - "execution_count": 144, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - " ID Year_Birth Age Cust_for Education Marital_Status Income \\\n", - "0 5524 1957.0 57.0 663 Graduation Single 58138.0 \n", - "8 4855 1974.0 40.0 388 PhD Together 30351.0 \n", - "15 2114 1946.0 68.0 582 PhD Single 82800.0 \n", - "33 7373 1952.0 62.0 608 PhD Divorced 46610.0 \n", - "39 2968 1943.0 71.0 513 PhD Divorced 48948.0 \n", - "... ... ... ... ... ... ... ... \n", - "2193 8722 1957.0 57.0 600 Master Married 82347.0 \n", - "2194 7118 1957.0 57.0 697 Graduation Married 73803.0 \n", - "2198 2632 1954.0 60.0 376 Graduation Married 50501.0 \n", - "2221 7366 1982.0 32.0 360 Master Single 75777.0 \n", - "2239 9405 1954.0 60.0 622 PhD Married 52869.0 \n", - "\n", - " Kidhome Teenhome Dt_Customer ... NumCatalogPurchases \\\n", - "0 0 0 2012-09-04 ... 10 \n", - "8 1 0 2013-06-06 ... 0 \n", - "15 0 0 2012-11-24 ... 6 \n", - "33 0 2 2012-10-29 ... 1 \n", - "39 0 0 2013-02-01 ... 10 \n", - "... ... ... ... ... ... \n", - "2193 0 0 2012-11-06 ... 7 \n", - "2194 0 1 2012-08-01 ... 5 \n", - "2198 1 1 2013-06-18 ... 4 \n", - "2221 0 0 2013-07-04 ... 6 \n", - "2239 1 1 2012-10-15 ... 1 \n", - "\n", - " NumStorePurchases NumWebVisitsMonth AcceptedCmp3 AcceptedCmp4 \\\n", - "0 4 7 0 0 \n", - "8 2 9 0 0 \n", - "15 12 3 0 0 \n", - "33 6 6 0 0 \n", - "39 5 6 1 0 \n", - "... ... ... ... ... \n", - "2193 10 3 1 0 \n", - "2194 6 6 1 0 \n", - "2198 4 6 1 0 \n", - "2221 11 1 0 1 \n", - "2239 4 7 0 0 \n", - "\n", - " AcceptedCmp5 AcceptedCmp1 AcceptedCmp2 Complain Response \n", - "0 0 0 0 0 1 \n", - "8 0 0 0 0 1 \n", - "15 1 1 0 0 1 \n", - "33 0 0 0 0 1 \n", - "39 0 0 0 0 1 \n", - "... ... ... ... ... ... \n", - "2193 0 1 0 0 1 \n", - "2194 0 0 0 0 1 \n", - "2198 0 0 0 0 1 \n", - "2221 1 0 0 0 1 \n", - "2239 0 0 0 0 1 \n", - "\n", - "[331 rows x 32 columns]" - ], - "text/html": [ - "\n", - "
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241411965.049.0312GraduationTogether71613.0002013-08-21...21040000000
361821984.030.0139GraduationTogether26646.0102014-02-10...0460000000
453241981.033.0161PhDMarried58293.0102014-01-19...3650000000
574461967.047.0293MasterTogether62513.0012013-09-09...41060000000
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223483721974.040.0363GraduationMarried34421.0102013-07-01...0270000000
2235108701967.047.0381GraduationMarried61223.0012013-06-13...3450000000
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1902 rows × 32 columns

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\n", - " " - ] - }, - "metadata": {}, - "execution_count": 145 - } - ] - }, - { - "cell_type": "markdown", - "source": [ - "####**ATENÇÃO: AINDA ESTOU TRABALHANDO NA PARTE ABAIXO**" - ], - "metadata": { - "id": "yHXCS6d37ii5" - } - }, - { - "cell_type": "code", - "source": [ - "## Meu primeiro objetivo é analisar demograficamente, ainda que de forma superficial, as duas populações.\n", - "## Para isso, vamos plotar alguns gráficos para entender melhor ambos os públicos.\n", - "\n", - "fig, axes= plt.subplots(6,2,figsize=[15,30])\n", - "\n", - "## Idade:\n", - "hg_age_r0=sns.histplot(x='Age',data=df_r0,ax=axes[0,0],stat='percent',kde=True,color='#8B4500')\n", - "hg_age_r0.set_title('Idade (R=0)', fontsize=12)\n", - "hg_age_r1=sns.histplot(x='Age',data=df_r1,ax=axes[0,1],stat='percent',kde=True,color='#FF8247')\n", - "hg_age_r1.set_title('Idade (R=1)', fontsize=12)\n", - "\n", - "## Educação:\n", - "hg_ed_r0=sns.histplot(x='Education',data=df_r0,ax=axes[1,0],stat='percent',kde=True,color='#104E8B')\n", - "hg_ed_r0.set_title('Educação (R=0)', fontsize=12)\n", - "hg_ed_r1=sns.histplot(x='Education',data=df_r1,ax=axes[1,1],stat='percent',kde=True,color='Skyblue') ##\t#87CEFF\n", - "hg_ed_r1.set_title('Educação (R=1)', fontsize=12)\n", - "\n", - "## Estado Civil:\n", - "hg_ec_r0=sns.histplot(x='Marital_Status',data=df_r0,ax=axes[2,0],stat='percent',kde=True,color='#FFD700')\n", - "hg_ec_r0.set_title('Estado Civil (R=0)', fontsize=12)\n", - "hg_ec_r1=sns.histplot(x='Marital_Status',data=df_r1,ax=axes[2,1],stat='percent',kde=True,color='#FFF68F') ##\t#87CEFF\n", - "hg_ec_r1.set_title('Estado Civil (R=1)', fontsize=12)\n", - "\n", - "## Renda:\n", - "hg_inc_r0=sns.histplot(x='Income',data=df_r0,ax=axes[3,0],stat='percent',kde=True,color='#00CD66')\n", - "hg_inc_r0.set_title('Renda (R=0)', fontsize=12)\n", - "hg_inc_r1=sns.histplot(x='Income',data=df_r1,ax=axes[3,1],stat='percent',kde=True,color='#00FA9A') ##\t#87CEFF\n", - "hg_inc_r1.set_title('Renda (R=1)', fontsize=12)\n", - "\n", - "## Crianças:\n", - "hg_kid_r0=sns.histplot(x='Kidhome',data=df_r0,ax=axes[4,0],stat='percent',kde=True,color='#8B4789')\n", - "hg_kid_r0.set_title('Crianças em Casa (R=0)', fontsize=12)\n", - "hg_kid_r1=sns.histplot(x='Kidhome',data=df_r1,ax=axes[4,1],stat='percent',kde=True,color='#FF83FA') ##\t#87CEFF\n", - "hg_kid_r1.set_title('Crianças em Casa (R=1)', fontsize=12)\n", - "\n", - "## Adolescentes:\n", - "hg_teen_r0=sns.histplot(x='Teenhome',data=df_r0,ax=axes[5,0],stat='percent',kde=True,color='#8B8682')\n", - "hg_teen_r0.set_title('Adolescentes em Casa (R=0)', fontsize=12)\n", - "hg_teen_r1=sns.histplot(x='Teenhome',data=df_r1,ax=axes[5,1],stat='percent',kde=True,color='#FFF5EE') ##\t#87CEFF\n", - "hg_teen_r1.set_title('Adolescentes em Casa (R=1)', fontsize=12)\n", - "\n", - "##\n", - "\n", - "fig.suptitle('Histogramas de Distribuição Demográfica das Duas Populações', fontsize=20,x=0.5,y=1.03)\n", - "fig.tight_layout()\n", - "\n", - "plt.show()\n" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 1000 - }, - "id": "BxikQb6T7pHi", - "outputId": "1abd8563-4195-427a-dfac-2596302c15d2" - }, - "execution_count": 146, - "outputs": [ - { - "output_type": "display_data", - "data": { - "text/plain": [ - "
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- }, - "metadata": { - "needs_background": "light" - } - } - ] - }, - { - "cell_type": "markdown", - "source": [ - "**Conclusões inicais da análise demográfica:**\n", - "\n", - "1. Os clientes que adquiriram o produto têm entre 18 e 71 anos. Porém, mais de 90% deles têm entre 24 e 66 anos e cerca de 75% entre 29 e 61 anos.\n", - "\n", - "\n", - "2. Mais de 95% deles possui renda entre 18.000 e 96.000 UM e mais de 85% entre 28 mil e 96 mil UM.\n", - "\n", - "3. A respeito do nível de escolaridade, aproximadamente 100% dos clientes que adquiriram o produto tem nível de pós-graduação, mestrado ou PhD, sendo que mais de 70% são pós-graduados ou possuem PhD.\n", - "\n", - "\n", - "4. Sobre o estado civil, mais de 90% dos clientes que adquiriram o produto não são viúvos, sendo que aproximamente 60% deles são solteiros ou casados e 30% divorciados ou em união estável.\n", - "\n", - "5. Sobre filhos, cerca de 65% dos clientes que adquiriram o produto não tem crianças em casa e cerca de 34% deles tem apenas uma. Sobre adolescentes, cerca de 70% dos clientes não tem nenhum em casa e 28% tem apenas um.\n", - "\n", - "6. Analisando os gráficos do grupo que não adquiriu o produto, podemos observar que os que adquiriram tem algumas tendências semelhantes, mas outras características bem divergentes, principalmente em relaçao à faixa de renda (1.000-160.000) e a composição do estado civil, como veremos mais especificamente abaixo. " - ], - "metadata": { - "id": "EY2cc3f-QUkP" - } - }, - { - "cell_type": "code", - "source": [ - "## Agora vamos complementar esses gráficos com uma análise percentual mais precisa" - ], - "metadata": { - "id": "KSDe-MrXpGoD" - }, - "execution_count": 147, - "outputs": [] - }, - { - "cell_type": "code", - "source": [ - "df_r1['Education'].value_counts(normalize=True).round(4)*100" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "C6VX31EeNAsp", - "outputId": "04dbce78-24f9-4c5c-fe52-9cd93a385030" - }, - "execution_count": 148, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "Graduation 45.92\n", - "PhD 29.61\n", - "Master 23.87\n", - "Basic 0.60\n", - "Name: Education, dtype: float64" - ] - }, - "metadata": {}, - "execution_count": 148 - } - ] - }, - { - "cell_type": "code", - "source": [ - "df_r1['Marital_Status'].value_counts(normalize=True).round(4)*100" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "GRS_R7JwNUr4", - "outputId": "3cd34133-c8ca-4c56-b7a0-d771c71569ae" - }, - "execution_count": 149, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "Single 32.33\n", - "Married 29.61\n", - "Together 18.13\n", - "Divorced 14.20\n", - "Widow 5.74\n", - "Name: Marital_Status, dtype: float64" - ] - }, - "metadata": {}, - "execution_count": 149 - } - ] - }, - { - "cell_type": "code", - "source": [ - "## No caso dos clientes que adquiriram o produto, em termos de variáveis categóricas, podemos observar que:\n", - "## Mais de 99% dos clientes ou tem graduação (45,92%), ou PhD (29,6%) ou mestrado (23,87%) completo.\n", - "## Além disso, mais de 75% deles são solteiros (32,33%), casados (29,61%) ou estão em situação de união estável (18,13%). Mas um percentual não desprezível (14.20%) está divorciado." - ], - "metadata": { - "id": "446Wk6E_Nsph" - }, - "execution_count": 150, - "outputs": [] - }, - { - "cell_type": "code", - "source": [ - "## Comparando essa análise com a mesma para os clientes que não adquiriram o produto, temos:\n", - "df_r0['Education'].value_counts(normalize=True).round(4)*100" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "Rc43hHQPP-qZ", - "outputId": "adc4c4d3-190f-4b94-b824-6a023897d438" - }, - "execution_count": 151, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "Graduation 51.21\n", - "Master 25.92\n", - "PhD 20.14\n", - "Basic 2.73\n", - "Name: Education, dtype: float64" - ] - }, - "metadata": {}, - "execution_count": 151 - } - ] - }, - { - "cell_type": "code", - "source": [ - "df_r0['Marital_Status'].value_counts(normalize=True).round(4)*100" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "VHBWHBGfQK5_", - "outputId": "2dcd1b16-253b-44d3-9222-1d2e795c35b2" - }, - "execution_count": 152, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "Married 40.27\n", - "Together 27.34\n", - "Single 19.66\n", - "Divorced 9.67\n", - "Widow 3.05\n", - "Name: Marital_Status, dtype: float64" - ] - }, - "metadata": {}, - "execution_count": 152 - } - ] - }, - { - "cell_type": "code", - "source": [ - "## As conclusões são um pouco parecidas inicialmente, mas podemos observar algumas diferenças.\n", - "\n", - "## O grupo de graduados, mestres e PhDs representa 97% dos clientes, percentual superior aos 90% do outro grupo, mas com uma diferença na composição relevante: \n", - "## A parcela de clientes PhDs é de 20,1% (ou aprox. -9%) e a de graduados é de (51,2% ou aprox +6%).\n", - "\n", - "## Em termos de estado civil, este grupo contém um percentual muito maior de casados (40,27% ou ~+11%) e de pessoas em união estável (27,34% ou ~+9%) do que o anterior.\n", - "## Por outro lado, o percentual de solteiros dessa amostra foi bem menor (19,66% ou -~13%). \n", - "## Juntos, esses 3 primeiros grupos representam mais de 85% da amostra, 10% a mais do que o caso anterior.\n" - ], - "metadata": { - "id": "CY8E0tleQRqZ" - }, - "execution_count": 153, - "outputs": [] - }, - { - "cell_type": "code", - "source": [ - "## Agora que entendemos sobre a composição demográfica, vamos aprofundar algumas análises.\n", - "## Será que os clientes com diferente escolaridade tiveram uma taxa de adesão muito diferente ao produto da 6ª campanha?\n", - "\n", - "pd.crosstab(df['Response'],df['Education'])" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 143 - }, - "id": "ELwAtzApjrdq", - "outputId": "ae136346-c024-4a48-c416-ddf6705d0fd9" - }, - "execution_count": 154, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "Education Basic Graduation Master PhD\n", - "Response \n", - "0 52 974 493 383\n", - "1 2 152 79 98" - ], - "text/html": [ - "\n", - "
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\n", - " " - ] - }, - "metadata": {}, - "execution_count": 154 - } - ] - }, - { - "cell_type": "code", - "source": [ - "## Vamos gerar uma visualização da taxa de adesão de cada grau de escolaridade.\n", - "\n", - "## Criando as taxas de cada grau com base nos dados do crosstab\n", - "Ad_Ed_B=((2/(2+52))*100)\n", - "Ad_Ed_G=((152/(152+974))*100)\n", - "Ad_Ed_M=((79/(79+493))*100)\n", - "Ad_Ed_P=((98/(98+383))*100)\n", - "\n", - "## Criando o D.F.\n", - "Ad_Educ=pd.DataFrame({\"Taxa de Adesão\": [Ad_Ed_B, Ad_Ed_G, Ad_Ed_M, Ad_Ed_P]}, \n", - "index=[\"Basic\", \"Graduation\", \"Master\", \"PhD\"])\n", - "\n", - "## Criando a visualização\n", - "plot1=Ad_Educ.plot(kind=\"bar\")\n", - "plot1.set_title('Taxa de Adesão Por Escolaridade', fontsize=12,)\n", - "\n", - "plt.show()" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 327 - }, - "id": "cQct42Z6nZyx", - "outputId": "27c48ff2-be58-4551-d0d3-d5eb72e29b66" - }, - "execution_count": 155, - "outputs": [ - { - "output_type": "display_data", - "data": { - "text/plain": [ - "
" - ], - "image/png": 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/+c9ceumlPPnkk+1OX2r06NH8/Oc/B+CWW27h+eezY9tjxozhl7/8JStWZJt41apVLFmy+RVJH330UdauXcuyZctYvHgxixcv5itf+QrTp09n9OjR3Hjjjbz88susWbOG3/zmNwC8/e1vZ8899+S6664DsjeUhQsXAlnf/YgRI5g4cSINDQ0sXbqUF154gYEDB9KjRw9++tOfbjpOMGrUKGbMmMGGDRtobm5m9uzZDB8+vCbb2cyq15mzbgRcCTwUEd8paboJOBm4JP38dZnZbwP+u+QA7KHAV6qqOOnuo9atXXjhhYwYMYKGhgZGjBjBmjVrePXVV/nc5z7H1Vdfza677sqkSZM49dRT+d3vfld2+tbOP/98xo8fz7777ssHP/jBTZ9s9tlnHy666CIOPfRQNm7cSK9evfj+97/PHnvssWne6dOnM27cuM2Wd+yxx3LCCSfw9a9/nRNOOIHGxkZ23nnnTV1AANOmTePMM8/koosuYt26dZx44ok0NjZy7rnn8thjjxERjBkzhsbGRs466yyOPfZYrrnmGsaOHUvfvn0BGDduHPfccw+NjY1I4tvf/ja77LLLltjsZlYBlZ7ZUXYCaSQwB1gEbEyjv0rWT/8LYHdgCdnplaskDQPOiIjT0vynpukBvhkRV3dU1LBhw6L1jUceeugh3vOe93T297KtkF9D6w5v1tMrJc2PiGHl2jrco4+Iu4C2vr8+psz084DTSoavAq7qXKlmZlZrvgSCmVnB1VXQd9TNZFsvv3Zm+amboO/Tpw8rV650YNShluvR+3RMs3zUzUXNBg0aRFNTE83NzXmXYhVoucOUmXW/ugn6Xr16+e5EZmYVqJuuGzMzq4yD3sys4Bz0ZmYF56A3Mys4B72ZWcE56M3MCs5Bb2ZWcA56M7OCc9CbmRWcg97MrOAc9GZmBeegNzMruM7cM/Yq4OPAiojYL42bAeydJtkeWB0R+5eZdzGwBtgArG/rNldmZrbldObqlVOBy4BrWkZExAktzyVNAl5oZ/5DIuK5Sgs0M7PqdOaesbMlDSnXJknA8cBHaluWmZnVSrV99KOAZyPisTbaA7hd0nxJE9pbkKQJkuZJmuebi5iZ1U61QT8emN5O+8iIGAocDnxe0ui2JoyIKRExLCKGNTQ0VFmWmZm1qDjoJW0DHAPMaGuaiFiWfq4AbgCGV7o+MzOrTDV79B8FHo6IpnKNkvpK6tfyHDgUeKCK9ZmZWQU6DHpJ04F7gL0lNUn6bGo6kVbdNpJ2lTQzDQ4A7pK0ELgPuDkibq1d6WZm1hmdOetmfBvjTykz7mngiPT8CaCxyvrMzKxK/masmVnBOejNzArOQW9mVnAOejOzgnPQm5kVnIPezKzgHPRmZgXnoDczKzgHvZlZwTnozcwKzkFvZlZwDnozs4Jz0JuZFZyD3sys4Bz0ZmYF56A3Myu4ztxh6ipJKyQ9UDLuAknLJC1IjyPamHespEckPS7pvFoWbmZmndOZPfqpwNgy4/83IvZPj5mtGyX1BL4PHA7sA4yXtE81xZqZWdd1GPQRMRtYVcGyhwOPR8QTEfEacC1wdAXLMTOzKlTTR3+2pL+krp0dyrTvBiwtGW5K48qSNEHSPEnzmpubqyjLzMxKVRr0PwT+AdgfWA5MqraQiJgSEcMiYlhDQ0O1izMzs6SioI+IZyNiQ0RsBH5E1k3T2jJgcMnwoDTOzMy6UUVBL2lgyeA44IEyk80F9pK0p6TewInATZWsz8zMKrdNRxNImg4cDOwkqQk4HzhY0v5AAIuB09O0uwI/jogjImK9pLOB24CewFUR8eAW+S3MzKxNHQZ9RIwvM/rKNqZ9GjiiZHgm8IZTL83MrPv4m7FmZgXnoDczKzgHvZlZwTnozcwKzkFvZlZwDnozs4Lr8PRKM9uyhpx3c94ldMriS47MuwSrkPfozcwKzkFvZlZwDnozs4JzH711mfuUzeqL9+jNzArOQW9mVnAOejOzgnPQm5kVnIPezKzgHPRmZgXXYdBLukrSCkkPlIz7H0kPS/qLpBskbd/GvIslLZK0QNK8WhZuZmad05k9+qnA2FbjZgH7RcT7gEeBr7Qz/yERsX9EDKusRDMzq0aHQR8Rs4FVrcbdHhHr0+CfgEFboDYzM6uBWvTRnwrc0kZbALdLmi9pQnsLkTRB0jxJ85qbm2tQlpmZQZVBL+k/gfXAtDYmGRkRQ4HDgc9LGt3WsiJiSkQMi4hhDQ0N1ZRlZmYlKg56SacAHwdOiogoN01ELEs/VwA3AMMrXZ+ZmVWmoqCXNBb4D+CoiHipjWn6SurX8hw4FHig3LRmZrbldOb0yunAPcDekpokfRa4DOgHzEqnTl6ept1V0sw06wDgLkkLgfuAmyPi1i3yW5iZWZs6vExxRIwvM/rKNqZ9GjgiPX8CaKyqOjMzq5q/GWtmVnAOejOzgnPQm5kVnIPezKzgHPRmZgXnoDczKzgHvZlZwTnozcwKzkFvZlZwDnozs4Jz0JuZFZyD3sys4Bz0ZmYF56A3Mys4B72ZWcE56M3MCq5TQS/pKkkrJD1QMm5HSbMkPZZ+7tDGvCenaR6TdHKtCjczs87p7B79VGBsq3HnAXdExF7AHWl4M5J2BM4HRpDdGPz8tt4QzMxsy+hU0EfEbGBVq9FHAz9Jz38CfLLMrIcBsyJiVUQ8D8zijW8YZma2BVXTRz8gIpan58+Q3Qy8td2ApSXDTWncG0iaIGmepHnNzc1VlGVmZqVqcjA2IgKIKpcxJSKGRcSwhoaGWpRlZmZUF/TPShoIkH6uKDPNMmBwyfCgNM7MzLpJNUF/E9ByFs3JwK/LTHMbcKikHdJB2EPTODMz6yadPb1yOnAPsLekJkmfBS4BPibpMeCjaRhJwyT9GCAiVgEXAnPTY2IaZ2Zm3WSbzkwUEePbaBpTZtp5wGklw1cBV1VUnZmZVc3fjDUzKzgHvZlZwTnozcwKzkFvZlZwDnozs4Jz0JuZFZyD3sys4Bz0ZmYF56A3Mys4B72ZWcE56M3MCs5Bb2ZWcA56M7OCc9CbmRWcg97MrOAc9GZmBVdx0EvaW9KCkseLkr7QapqDJb1QMs3Xqy/ZzMy6olN3mConIh4B9geQ1JPspt83lJl0TkR8vNL1mJlZdWrVdTMG+FtELKnR8szMrEZqFfQnAtPbaPuApIWSbpG0b43WZ2ZmnVR10EvqDRwFXFem+X5gj4hoBCYDN7aznAmS5kma19zcXG1ZZmaW1GKP/nDg/oh4tnVDRLwYEWvT85lAL0k7lVtIREyJiGERMayhoaEGZZmZGdQm6MfTRreNpF0kKT0fnta3sgbrNDOzTqr4rBsASX2BjwGnl4w7AyAiLgc+BZwpaT3wMnBiREQ16zQzs66pKugj4u9A/1bjLi95fhlwWTXrMDOz6vibsWZmBeegNzMrOAe9mVnBOejNzArOQW9mVnAOejOzgnPQm5kVnIPezKzgHPRmZgXnoDczKzgHvZlZwTnozcwKzkFvZlZwDnozs4Jz0JuZFZyD3sys4Bz0ZmYFV3XQS1osaZGkBZLmlWmXpO9JelzSXyQNrXadZmbWeVXdSrDEIRHxXBtthwN7pccI4Ifpp5mZdYPu6Lo5GrgmMn8Ctpc0sBvWa2Zm1CboA7hd0nxJE8q07wYsLRluSuM2I2mCpHmS5jU3N9egLDMzg9oE/ciIGErWRfN5SaMrWUhETImIYRExrKGhoQZlmZkZ1CDoI2JZ+rkCuAEY3mqSZcDgkuFBaZyZmXWDqoJeUl9J/VqeA4cCD7Sa7Cbg0+nsm4OAFyJieTXrNTOzzqv2rJsBwA2SWpb184i4VdIZABFxOTATOAJ4HHgJ+EyV6zQzsy6oKugj4gmgscz4y0ueB/D5atZjZmaV8zdjzcwKzkFvZlZwDnozs4Jz0JuZFZyD3sys4Bz0ZmYF56A3Myu4Wl2meKs35Lyb8y6hQ4svOTLvEsysgLxHb2ZWcA56M7OCc9CbmRWcg97MrOAc9GZmBeegNzMrOAe9mVnBOejNzAqu4qCXNFjS7yX9VdKDkv61zDQHS3pB0oL0+Hp15ZqZWVdV883Y9cAXI+L+dN/Y+ZJmRcRfW003JyI+XsV6zMysChXv0UfE8oi4Pz1fAzwE7FarwszMrDZq0kcvaQjwfuDeMs0fkLRQ0i2S9m1nGRMkzZM0r7m5uRZlmZkZNQh6SdsCvwK+EBEvtmq+H9gjIhqBycCNbS0nIqZExLCIGNbQ0FBtWWZmllQV9JJ6kYX8tIi4vnV7RLwYEWvT85lAL0k7VbNOMzPrmmrOuhFwJfBQRHynjWl2SdMhaXha38pK12lmZl1XzVk3HwL+GVgkaUEa91Vgd4CIuBz4FHCmpPXAy8CJERFVrNPMzLqo4qCPiLsAdTDNZcBlla7DzMyq52/GmpkVnIPezKzgHPRmZgXnoDczKzgHvZlZwTnozcwKzkFvZlZwDnozs4Jz0JuZFZyD3sys4Bz0ZmYF56A3Mys4B72ZWcE56M3MCs5Bb2ZWcA56M7OCc9CbmRVctTcHHyvpEUmPSzqvTPtbJM1I7fdKGlLN+szMrOuquTl4T+D7wOHAPsB4Sfu0muyzwPMR8U7gf4FvVbo+MzOrTDV79MOBxyPiiYh4DbgWOLrVNEcDP0nPfwmMkdTufWbNzKy2Kr45OLAbsLRkuAkY0dY0EbFe0gtAf+C51guTNAGYkAbXSnqkitq6w06U+T2qoTf35x1vz9ry9qytmm7PLbQt92iroZqgr6mImAJMybuOzpI0LyKG5V1HUXh71pa3Z23V+/asputmGTC4ZHhQGld2GknbANsBK6tYp5mZdVE1QT8X2EvSnpJ6AycCN7Wa5ibg5PT8U8DvIiKqWKeZmXVRxV03qc/9bOA2oCdwVUQ8KGkiMC8ibgKuBH4q6XFgFdmbQVHUTTdTnfD2rC1vz9qq6+0p72CbmRWbvxlrZlZwDnozs4Jz0JuZFZyD3qwAlBnc8ZT2ZuSDsV0gaRZwXESsTsM7ANdGxGH5Vla/0jWTBlByBlhEPJVfRfVL0qKIeG/edRRB+t7P4cC706iHgFsjYn1+VVVuq/lmbJ3YqSXkASLieUk751lQPZP0L8D5wLPAxjQ6gPflVlR9u1/SgRExN+9C6pmk3YDfAcuBPwMCPg5MknRIRDydZ32V8B59F0iaD4xr2eOUtAdwQ0QMzbey+pS+XzEiIvxt6RqQ9DDwTmAJ8HeygIqI8BtnF0iaCiyIiO+2Gn8OcEBEnFx2xq2Y9+i75j+BuyT9geyfaBSvX4jNum4p8ELeRRSIuxBr46CIOKX1yIj4Xh1cbLEsB30XRMStkoYCB6VRX4iIml4h8E3mCeBOSTcDr7aMjIjv5FdS/YqIJZJGAntFxNWSGoBt866rDr3cTttL3VZFDTnoO0HSuyPi4RTyAC19dLtL2j0i7s+rtjr3VHr0Tg+rgqTzgWHA3sDVQC/gZ8CH8qyrDm0n6Zgy4wW8vbuLqQX30XeCpCkRMUHS78s0R0R8pNuLKhBJ2wJExNq8a6lnkhYA7wfuj4j3p3F/cR9910i6ur32iPhMd9VSK96j74SImJB+HpJ3LUUiaT/gp8COafg54NMR8WCuhdWv1yIiJAWApL55F1SP6jHIO+Kg7wJJx5GdS7tG0teAocCFEfHnnEurV1OAf4+I3wNIOhj4EfDBPIuqY7+QdAWwvaTPAacCP865prol6S3AscAQNv+ex8S8aqqUu266oOVjcDrgdRHwP8DXI6L1LRStEyQtjIjGjsZZ50n6GHAoWX/ybRExK+eS6pakW8nOCpsPbGgZHxGTciuqQt6j75qWF/tIYEpE3CzpojwLqnNPSPovsu4bgH8iOxPHKiDpWxHxZWBWmXHWdYMiYmzeRdSCr3XTNcvSR+MTgJnpo523YeVOBRqA69OjIY2zynyszLjDu72K4rhbUiEuKeGumy6Q9DZgLLAoIh6TNBB4b0TcnnNp9iYm6UzgLOAdwN9KmvoBf4yIf8qlsDolaRHZpTi2AfYi+5T5KnX8TWMHfQXS9W36tAz7IlxdI+m7EfEFSb8h+4faTEQclUNZdUvSdsAOwMXAeSVNayJiVT5V1a90aZM2RcSS7qqlVgSc1esAAAUeSURBVBz0XSDpKGASsCuwAtgdeDgi9s21sDoj6YCImC/pw+XaI+IP3V1TEUj6B6ApIl5NZzC9D7im9EJ81jFJfYAzyK4btAi4sl6vWtnC/ctdcyHZ5Q8ejYg9gY8Cf8q3pPoTEfPT0/0j4g+lD2D/PGurc78CNkh6J9mpq4OBn+dbUl36Cdk3jBeRHeOou7NsWnPQd826dKXFHpJ6pPO/h+VdVB0rdxXAU7q7iALZmPY8jwEmR8S5wMCca6pH+0TEP0XEFcCnyC5eWNd8emXXrE5f158NTJO0guxysNYFksYD/wjsKemmkqZ+gPuUK7cubdtPA59I43rlWE+9WtfyJCLWS8qzlppwH30XpK+Uv0z2SegkYDtgmq+n3jXpYNeelDl4CPyl3vtD8yJpH7K+5XsiYrqkPYHjI+JbOZdWVyRt4PUdOAFvJbtqZctZN3V3YTMHfYUk7QSsDG9AM9vKuY++EyQdJOlOSddLer+kB4AHgGclFeKbc3lI23WupLWSXpO0QdKLeddVryTtJemXkv4q6YmWR951Wf4c9J1zGfDfwHSye0meFhG7AKPJuh+sMpcB44HHyD4enwZ8P9eK6tvVwA+B9cAhwDVk16O3NzkHfedsExG3R8R1wDMR8SeAiHg457rqXkQ8DvSMiA0RcTXZN4+tMm+NiDvIumSXRMQFZNdlsjc5n3XTORtLnre+zZj76Cv3kqTewAJJ3waW452ParwqqQfwmKSzgWX4VoKGD8Z2SslR+NIj8KThPhHhU9gqkM6+WUF2CuC/kZ3F9IO0l29dJOlA4CFge7Iv920HfLvlE6i9eTnozcwKzl03lhtJT1L+ombvyKGcutXqS2dv4IvEmYPe8lR6+Yg+wHGk+8dal3wAWEp2Vti9ZF2KZpu468a2KpLmR8QBeddRTyT1JLvpyHiyK1beDEz3TdathffoLTeShpYM9iDbw/ffZBdFxAbgVuDWdNez8cCdkr4REZflW51tDfxPZXkqvfzremAxcHw+pdS3FPBHkoX8EOB7wA151mRbD3fdmNU5SdcA+wEzgWsj4oGcS7KtjIPeup2kf2+vPSK+0121FIGkjbx+tcXSf+i6vdqi1Za7biwP/dLPvYEDgZbTAz8B3JdLRXUsIvxtYmuX9+gtN5JmA0dGxJo03A+4OSJG51uZWbF4T8DyNAB4rWT4tTTOzGrIXTeWp2uA+yS1nB3ySbIbM5tZDbnrxnIl6QBgZBqcHRF/zrMesyJy0FvuJO1MdgkEACLiqRzLMSsc99FbbiQdJekx4EngD+nnLflWZVY8DnrL04XAQcCjEbEn8FHA1043qzEHveVpXUSsBHpI6hERv2fzK1qaWQ34rBvL02pJ2wKzgWmSVvD6NzzNrEZ8MNZyI6kv2T14ewAnkd36blrayzezGnHQWy7SNdR/GxGH5F2LWdG5j95yka6hvlHSdnnXYlZ07qO3PK0FFkmaRUnffESck19JZsXjoLc8XZ8e8PrldX2/U7Mac9Bbt5N0NDAoIr6fhu8DGsjC/st51mZWRO6jtzz8B69fgx6gN3AAcDBwRh4FmRWZ9+gtD70jYmnJ8F0RsQpYlU65NLMa8h695WGH0oGIOLtksKGbazErPAe95eFeSZ9rPVLS6fhWgmY15y9MWbdLlyW+EXgVuD+NPgB4C/DJiHg2r9rMishBb7mR9BFg3zT4YET8Ls96zIrKQW9mVnDuozczKzgHvZlZwTnozcwKzkFvZlZw/x+CyNiOOEJyQAAAAABJRU5ErkJggg==\n" - }, - "metadata": { - "needs_background": "light" - } - } - ] - }, - { - "cell_type": "code", - "source": [ - "## E quando analisamos a mesma coisa pelo estado civil?\n", - "\n", - "pd.crosstab(df['Response'],df['Marital_Status'])" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 143 - }, - "id": "JwIlViLFFSPf", - "outputId": "d679b7b6-8892-43f9-fdfb-4daa4a6d2a0b" - }, - "execution_count": 156, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "Marital_Status Divorced Married Single Together Widow\n", - "Response \n", - "0 184 766 374 520 58\n", - "1 47 98 107 60 19" - ], - "text/html": [ - "\n", - "
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Marital_StatusDivorcedMarriedSingleTogetherWidow
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\n", - " " - ] - }, - "metadata": {}, - "execution_count": 156 - } - ] - }, - { - "cell_type": "code", - "source": [ - "## Vamos gerar uma visualização da taxa de adesão de cada estado civil.\n", - "\n", - "## Criando as taxas de cada grau com base nos dados do crosstab\n", - "Ad_M_S=((107/(107+374))*100)\n", - "Ad_M_T=((60/(60+520))*100)\n", - "Ad_M_M=((98/(98+766))*100)\n", - "Ad_M_D=((47/(47+184))*100)\n", - "Ad_M_W=((19/(19+58))*100)\n", - "\n", - "## Criando o D.F.\n", - "Ad_MS=pd.DataFrame({\"Taxa de Adesão\": [Ad_M_S, Ad_M_T, Ad_M_M, Ad_M_D, Ad_M_W]}, \n", - "index=[\"Single\", \"Together\", \"Married\", \"Divorced\", 'Widow'])\n", - "\n", - "## Criando a visualização\n", - "plot1=Ad_MS.plot(kind=\"bar\",color='#DC143C')\n", - "plot1.set_title('Taxa de Adesão Por Escolaridade', fontsize=12,)\n", - "\n", - "plt.show()" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 316 - }, - "id": "GwNi-zjPE9xt", - "outputId": "fc5ef29d-2baa-4a02-f102-fcaaa3e8222e" - }, - "execution_count": 157, - "outputs": [ - { - "output_type": "display_data", - "data": { - "text/plain": [ - "
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- }, - "metadata": { - "needs_background": "light" - } - } - ] - }, - { - "cell_type": "code", - "source": [ - "## A taxa de adesão muda bastante conforme as categorias de escolaridade e estado civil.\n", - "## Vamos ver se a correlação é capaz de capturar isso também. " - ], - "metadata": { - "id": "gGNTHt2YuaGx" - }, - "execution_count": 158, - "outputs": [] - }, - { - "cell_type": "code", - "source": [ - "df_3[['Education','Response']].corr().round(2)" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 112 - }, - "id": "KF9UzlNYs_By", - "outputId": "05bab0f4-7d94-49b0-afa2-40a768f919ce" - }, - "execution_count": 159, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - " Education Response\n", - "Education 1.00 0.08\n", - "Response 0.08 1.00" - ], - "text/html": [ - "\n", - "
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Ec_Together-0.311.00-0.47-0.20-0.11-0.07
Ec_Married-0.42-0.471.00-0.27-0.15-0.08
Ec_Divorced-0.18-0.20-0.271.00-0.060.05
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\n", - " " - ] - }, - "metadata": {}, - "execution_count": 160 - } - ] - }, - { - "cell_type": "code", - "source": [ - "## Em ambos os casos, a correlação foi baixa para todas as variáveis." - ], - "metadata": { - "id": "WogKC12Ju0M7" - }, - "execution_count": 161, - "outputs": [] - }, - { - "cell_type": "code", - "source": [ - "## Agora, vamos aprofundar mais um pouco na análise incluindo também hábitos de consumo.\n", - "## Para isso, vamos plotar alguns gráficos.\n", - "\n", - "janela, graficos= plt.subplots(3,2,figsize=[15,20])\n", - "\n", - "## Educação, Renda e Estado Civil:\n", - "sns.boxplot(x='Income',y='Education',hue='Marital_Status',data=df_r0,order=['Basic', 'Graduation', 'Master', 'PhD'],hue_order=['Single','Together','Married','Divorced','Widow'],ax=graficos[0,0],palette='mako').set_title('Renda Por Escolaridade e Estado Civil (R=0)', fontsize=12)\n", - "sns.boxplot(x='Income',y='Education',hue='Marital_Status',data=df_r1,order=['Basic', 'Graduation', 'Master', 'PhD'],hue_order=['Single','Together','Married','Divorced','Widow'],ax=graficos[0,1],palette='mako').set_title('Renda Por Escolaridade e Estado Civil (R=1)', fontsize=12)\n", - "\n", - "## Total de compras:\n", - "sns.boxplot(x='NumPurchases',y='Education',hue='Marital_Status',data=df_r0,order=['Basic', 'Graduation', 'Master', 'PhD'],hue_order=['Single','Together','Married','Divorced','Widow'],ax=graficos[1,0],palette='bright').set_title('Compras Por Escolaridade e Estado Civil (R=0)', fontsize=12)\n", - "sns.boxplot(x='NumPurchases',y='Education',hue='Marital_Status',data=df_r1,order=['Basic', 'Graduation', 'Master', 'PhD'],hue_order=['Single','Together','Married','Divorced','Widow'],ax=graficos[1,1],palette='bright').set_title('Compras Por Escolaridade e Estado Civil (R=1)', fontsize=12)\n", - "\n", - "## Consumo Total\n", - "sns.boxplot(x='MntTotal',y='Education',hue='Marital_Status',data=df_r0,order=['Basic', 'Graduation', 'Master', 'PhD'],hue_order=['Single','Together','Married','Divorced','Widow'],ax=graficos[2,0],palette='pastel').set_title('Consumo Total Por Escolaridade e Estado Civil (R=0)', fontsize=12)\n", - "sns.boxplot(x='MntTotal',y='Education',hue='Marital_Status',data=df_r1,order=['Basic', 'Graduation', 'Master', 'PhD'],hue_order=['Single','Together','Married','Divorced','Widow'],ax=graficos[2,1],palette='pastel').set_title('Consumo Total Por Escolaridade e Estado Civil (R=1)', fontsize=12)\n", - "\n", - "## Configurações finais do plot\n", - "janela.suptitle('BoxPlots de Características Demográficas', fontsize=20,x=0.5,y=1.03)\n", - "janela.tight_layout()\n", - "\n", - "plt.show()" - ], - "metadata": { - "id": "KC2Vm8u87pEJ", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 1000 - }, - "outputId": "99c78fa7-bd22-4e30-f298-c4ff92b1dee1" - }, - "execution_count": 162, - "outputs": [ - { - "output_type": "display_data", - "data": { - "text/plain": [ - "
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\n" - }, - "metadata": { - "needs_background": "light" - } - } - ] - }, - { - "cell_type": "code", - "source": [], - "metadata": { - "id": "jMiodTrbO8Aw" - }, - "execution_count": 162, - "outputs": [] - }, - { - "cell_type": "code", - "source": [], - "metadata": { - "id": "ColCoAMFDW9G" - }, - "execution_count": 162, - "outputs": [] - }, - { - "cell_type": "markdown", - "source": [ - "##### **IDEIA AINDA EM FORMULAÇÃO**: INSERIR PARTE ALTERNATIVA COM AUTOMAÇÃO DO PANDAS PROFILING" - ], - "metadata": { - "id": "P8UEUtsCEdqz" - } - }, - { - "cell_type": "code", - "source": [ - "!pip install https://github.com/pandas-profiling/pandas-profiling/archive/master.zip" - ], - "metadata": { - "id": "7QuHfW3ixZNj", - "colab": { - "base_uri": "https://localhost:8080/" - }, - "outputId": "a3292f98-a532-41fe-964f-d32fab3fab5e" - }, - "execution_count": 163, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n", - "Collecting https://github.com/pandas-profiling/pandas-profiling/archive/master.zip\n", - " Downloading https://github.com/pandas-profiling/pandas-profiling/archive/master.zip (21.9 MB)\n", - "\u001b[K |████████████████████████████████| 21.9 MB 1.4 MB/s \n", - "\u001b[?25hRequirement already satisfied: joblib~=1.1.0 in /usr/local/lib/python3.7/dist-packages (from pandas-profiling==3.2.0) (1.1.0)\n", - "Requirement already satisfied: scipy>=1.4.1 in /usr/local/lib/python3.7/dist-packages (from pandas-profiling==3.2.0) (1.7.3)\n", - "Requirement already satisfied: pandas!=1.0.0,!=1.0.1,!=1.0.2,!=1.1.0,>=0.25.3 in /usr/local/lib/python3.7/dist-packages (from pandas-profiling==3.2.0) (1.3.5)\n", - "Requirement already satisfied: matplotlib>=3.2.0 in /usr/local/lib/python3.7/dist-packages (from pandas-profiling==3.2.0) (3.2.2)\n", - "Requirement already satisfied: pydantic>=1.8.1 in /usr/local/lib/python3.7/dist-packages (from pandas-profiling==3.2.0) (1.9.2)\n", - "Requirement already satisfied: PyYAML>=5.0.0 in /usr/local/lib/python3.7/dist-packages (from pandas-profiling==3.2.0) (6.0)\n", - "Requirement already satisfied: jinja2>=2.11.1 in /usr/local/lib/python3.7/dist-packages (from pandas-profiling==3.2.0) (2.11.3)\n", - "Collecting visions[type_image_path]==0.7.5\n", - " Downloading visions-0.7.5-py3-none-any.whl (102 kB)\n", - "\u001b[K |████████████████████████████████| 102 kB 28.2 MB/s \n", - "\u001b[?25hRequirement already satisfied: numpy>=1.16.0 in /usr/local/lib/python3.7/dist-packages (from pandas-profiling==3.2.0) (1.21.6)\n", - "Collecting htmlmin>=0.1.12\n", - " Downloading htmlmin-0.1.12.tar.gz (19 kB)\n", - "Requirement already satisfied: missingno>=0.4.2 in /usr/local/lib/python3.7/dist-packages (from pandas-profiling==3.2.0) (0.5.1)\n", - "Collecting phik>=0.11.1\n", - " Downloading phik-0.12.2-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (690 kB)\n", - "\u001b[K 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satisfied: attrs>=19.3.0 in /usr/local/lib/python3.7/dist-packages (from visions[type_image_path]==0.7.5->pandas-profiling==3.2.0) (22.1.0)\n", - "Collecting imagehash\n", - " Downloading ImageHash-4.2.1.tar.gz (812 kB)\n", - "\u001b[K |████████████████████████████████| 812 kB 67.4 MB/s \n", - "\u001b[?25hRequirement already satisfied: Pillow in /usr/local/lib/python3.7/dist-packages (from visions[type_image_path]==0.7.5->pandas-profiling==3.2.0) (7.1.2)\n", - "Requirement already satisfied: MarkupSafe>=0.23 in /usr/local/lib/python3.7/dist-packages (from jinja2>=2.11.1->pandas-profiling==3.2.0) (2.0.1)\n", - "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.7/dist-packages (from matplotlib>=3.2.0->pandas-profiling==3.2.0) (0.11.0)\n", - "Requirement already satisfied: python-dateutil>=2.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib>=3.2.0->pandas-profiling==3.2.0) (2.8.2)\n", - "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib>=3.2.0->pandas-profiling==3.2.0) (3.0.9)\n", - "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib>=3.2.0->pandas-profiling==3.2.0) (1.4.4)\n", - "Requirement already satisfied: typing-extensions in /usr/local/lib/python3.7/dist-packages (from kiwisolver>=1.0.1->matplotlib>=3.2.0->pandas-profiling==3.2.0) (4.1.1)\n", - "Requirement already satisfied: pytz>=2017.3 in /usr/local/lib/python3.7/dist-packages (from pandas!=1.0.0,!=1.0.1,!=1.0.2,!=1.1.0,>=0.25.3->pandas-profiling==3.2.0) (2022.2.1)\n", - "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.7/dist-packages (from python-dateutil>=2.1->matplotlib>=3.2.0->pandas-profiling==3.2.0) (1.15.0)\n", - "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests>=2.24.0->pandas-profiling==3.2.0) (2022.6.15)\n", - "Requirement already satisfied: charset-normalizer<3,>=2 in /usr/local/lib/python3.7/dist-packages (from requests>=2.24.0->pandas-profiling==3.2.0) (2.1.0)\n", - "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests>=2.24.0->pandas-profiling==3.2.0) (2.10)\n", - "Requirement already satisfied: urllib3<1.27,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests>=2.24.0->pandas-profiling==3.2.0) (1.24.3)\n", - "Requirement already satisfied: PyWavelets in /usr/local/lib/python3.7/dist-packages (from imagehash->visions[type_image_path]==0.7.5->pandas-profiling==3.2.0) (1.3.0)\n", - "Building wheels for collected packages: pandas-profiling, htmlmin, imagehash\n", - " Building wheel for pandas-profiling (setup.py) ... \u001b[?25l\u001b[?25hdone\n", - " Created wheel for pandas-profiling: filename=pandas_profiling-3.2.0-py2.py3-none-any.whl size=261257 sha256=e37d3f2d40d8cefa256431d4bf4ef13b2d3a1fa3ea8f00fbac9c5683e8a57225\n", - " Stored in directory: /tmp/pip-ephem-wheel-cache-3lx7foft/wheels/cc/d5/09/083fb07c9363a2f45854b0e3a7de7d7c560f07da74b9e9769d\n", - " Building wheel for htmlmin (setup.py) ... \u001b[?25l\u001b[?25hdone\n", - " Created wheel for htmlmin: filename=htmlmin-0.1.12-py3-none-any.whl size=27098 sha256=ab427bcba2a6ed18ae55326f398257bb76e2fee5c1ebcebb416a77000ea0d8e3\n", - " Stored in directory: /root/.cache/pip/wheels/70/e1/52/5b14d250ba868768823940c3229e9950d201a26d0bd3ee8655\n", - " Building wheel for imagehash (setup.py) ... \u001b[?25l\u001b[?25hdone\n", - " Created wheel for imagehash: filename=ImageHash-4.2.1-py2.py3-none-any.whl size=295206 sha256=4188663de6130c645d63cab844d5568bdffb51bc094f7a9d674a432fefdf555b\n", - " Stored in directory: /root/.cache/pip/wheels/4c/d5/59/5e3e297533ddb09407769762985d134135064c6831e29a914e\n", - "Successfully built pandas-profiling htmlmin imagehash\n", - "Installing collected packages: tangled-up-in-unicode, multimethod, visions, imagehash, requests, phik, htmlmin, pandas-profiling\n", - " Attempting uninstall: requests\n", - " Found existing installation: requests 2.23.0\n", - " Uninstalling requests-2.23.0:\n", - " Successfully uninstalled requests-2.23.0\n", - " Attempting uninstall: pandas-profiling\n", - " Found existing installation: pandas-profiling 1.4.1\n", - " Uninstalling pandas-profiling-1.4.1:\n", - " Successfully uninstalled pandas-profiling-1.4.1\n", - "Successfully installed htmlmin-0.1.12 imagehash-4.2.1 multimethod-1.8 pandas-profiling-3.2.0 phik-0.12.2 requests-2.28.1 tangled-up-in-unicode-0.2.0 visions-0.7.5\n" - ] - } - ] - }, - { - "cell_type": "code", - "source": [ - "from pandas_profiling import ProfileReport" - ], - "metadata": { - "id": "rE9LVlPDwABl" - }, - "execution_count": 164, - "outputs": [] - }, - { - "cell_type": "code", - "source": [ - "Pr= ProfileReport(df_r1)\n", - "Pr.to_file(output_file='PandasProfiling_v1.html')" - ], - "metadata": { - "id": "CUUMsFS3wLy8" - }, - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "code", - "source": [ - "Pr" - ], - "metadata": { - "id": "lLDgupU_yorj" - }, - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "code", - "source": [], - "metadata": { - "id": "QXVKPILwWMiK" - }, - "execution_count": null, - "outputs": [] - } - ] -} \ No newline at end of file From b6edc6a7ed07ca3c6efd78d4818cc93e4cd0e147 Mon Sep 17 00:00:00 2001 From: Marcilio Duarte <104692475+marcilioduarte@users.noreply.github.com> Date: Mon, 23 Jan 2023 00:01:13 -0300 Subject: [PATCH 18/18] Delete texto.txt --- texto.txt | 4 ---- 1 file changed, 4 deletions(-) delete mode 100644 texto.txt diff --git a/texto.txt b/texto.txt deleted file mode 100644 index 68b9e9c..0000000 --- a/texto.txt +++ /dev/null @@ -1,4 +0,0 @@ -What is Python language? -Python is a widely used high-level, general-purpose, interpreted, dynamic programming language.Its design philosophy emphasizes code readability, and its syntax allows programmers to express concepts in fewer lines of code than possible in -languages such as C++ or Java. -Python supports multiple programming paradigms, including object-oriented, imperative and functional programming or procedural styles.It features a dynamic type system and automatic memory management and has a large and comprehensive standard library.The best way we learn anything is by practice and exercise questions. We have started this section for those (beginner to intermediate) who are familiar with Python. \ No newline at end of file