diff --git a/fit.py b/fit.py
index 2e7c176..b3c67df 100644
--- a/fit.py
+++ b/fit.py
@@ -1,17 +1,27 @@
-from sklearn.ensemble import RandomForestClassifier
-from sklearn.model_selection import train_test_split
-from sklearn.feature_selection import SequentialFeatureSelector
+from sklearn.ensemble import RandomForestClassifier,VotingClassifier
+from sklearn.svm import SVC
+from sklearn.linear_model import LogisticRegression
+from sklearn.base import BaseEstimator
+from xgboost import XGBClassifier
+
+from sklearn.decomposition import PCA
+from sklearn.discriminant_analysis import LinearDiscriminantAnalysis as LDA
+
+from sklearn.model_selection import train_test_split,learning_curve,RandomizedSearchCV
 from sklearn.preprocessing import StandardScaler
 from sklearn.impute import SimpleImputer
 from sklearn.metrics import classification_report
-from sklearn.pipeline import Pipeline
+
+from imblearn.over_sampling import SMOTE
+from imblearn.pipeline import Pipeline
+
+import time
+import os
 import numpy as np
 import pandas as pd
-import os
 import joblib
-import time
 
-def data_collection(file_path = "Datasets/SDSS_DR18.csv"):
+def data_collection(file_path = "Datasets/SDSS_DR18.csv") -> np.ndarray:
   # read the raw CSV into a DataFrame
   df_raw = pd.read_csv(file_path)
 
@@ -49,58 +59,95 @@ def data_collection(file_path = "Datasets/SDSS_DR18.csv"):
   y = df.iloc[:,-1].to_numpy()    # Target Column
   x = df.iloc[:,:-1].to_numpy()     # Feature Column
   
-  return x,y,column_names
+  return x,y
 
-def model(x,y,column_names):
+def get_column_names(path="Datasets/SDSS_DR18.csv") -> np.ndarray:
+  df = pd.read_csv(path)
+  column_names = df.columns.to_numpy()
+  return column_names
+
+def model(x,y) -> BaseEstimator:
   # split data, keeping class balance in train/test
   x_train,x_test,y_train,y_test = train_test_split(
     x,y,test_size=2/10,random_state=120,shuffle=True,stratify=y
   )
 
-  # Random forest for final classification
-  rf_model = RandomForestClassifier(
-    n_estimators=150,max_depth=10,random_state=104,class_weight="balanced",n_jobs=-1)
-  sfs = SequentialFeatureSelector(
-    rf_model,n_features_to_select="auto",tol=0.007,direction="forward",cv=None)   
-  
-  # preprocessing: impute, scale, then reduce dimensionality
-  preprocessor = Pipeline([
-    ("imputation",SimpleImputer(strategy="median")),
-    ("scale", StandardScaler()),
-    ("sfs",sfs)
-  ])
-  # full pipeline: preprocessing followed by the classifier
+  # RF, SVC, LR, XGB
+  rf_model = RandomForestClassifier(random_state=40)
+  svc_model = SVC(random_state=41)
+  lr_model = LogisticRegression(random_state=42,max_iter=10_000)
+  xgb_model = XGBClassifier(random_state=43)
+
+  pca = PCA(random_state=44)
+  lda = LDA(n_components=2)
+
   pipe = Pipeline([
-    ("preprocessor",preprocessor),
+    ("impute",SimpleImputer(strategy="median")),
+    ("scale",StandardScaler()),
+    ("smote",SMOTE(random_state=101)),
+    ("dimen",pca),
     ("model",rf_model)
   ])
+  param_list = [
+    { # Random Forest, PCA On
+      "model": [rf_model],"model__n_estimators":np.arange(150,650,100),
+      "model__max_depth":np.arange(7,14,2), "dimen" : [pca], "dimen__n_components": np.arange(5,8,1)
+    },
+    { # Logistic Regression, No dimen. reduction, l1 penalty, `saga` solver
+      "model": [lr_model], "model__C": [0.01,0.1,1,10], "model__penalty":["l1"], "model__solver":["saga"],
+      "dimen": ["passthrough"]
+    },
+    { # Logistic Regression, No dimen. reduction, l2 penalty, `lbfgs` solver
+      "model": [lr_model], "model__C": [0.01,0.1,1,10], "model__penalty":["l2"], "model__solver":["lbfgs"],
+      "dimen": ["passthrough"]
+    },
+    { # XGBoost, PCA On
+      "dimen": [pca], "dimen__n_components": np.arange(5,8,1),
+      "model": [xgb_model], "model__n_estimators" : np.linspace(500,1100,3,dtype=int),"model__learning_rate": [0.01,0.1], "model__max_depth":np.arange(7,14,3)
+    },
+    { # XGBoost, LDA On
+      "dimen": [lda],
+      "model": [xgb_model], "model__n_estimators" : [500,700,900],"model__learning_rate": [0.01,0.1], "model__max_depth":np.arange(7,14,3)
+    },
+    { # XGBoost, No dimen. reduction
+      "dimen": ["passthrough"],
+      "model": [xgb_model], "model__n_estimators" : [500,700,900],"model__learning_rate": [0.01,0.1], "model__max_depth":np.arange(7,14,3)
+    }
+  ]
+
+  rscv = RandomizedSearchCV(
+    estimator=pipe,param_distributions=param_list,n_iter=8,cv=5,n_jobs=-1,random_state=50,refit=True
+  )
+
+  print(f"🤖 Starting Model Training....")
+  print(f"‼️ Training may take a lot of time, so please sit tight....")
   t1 = time.time()
-  pipe.fit(x_train,y_train)
-  print("Model is trained successfully ✅")
+  rscv.fit(x_train,y_train)
   t2 = time.time()
-  minutes, seconds = divmod((t2-t1),60)
-  print(f"Time taken for training: {minutes} Minute {seconds} Second")
-  # evaluate on the held-out test set
+  minutes,seconds = np.divmod((t2-t1),60)
+  print(f"⌛️ Time Elapsed: {minutes} Minutes {seconds:.2f} Seconds")
+  estimator = rscv.best_estimator_
   y_true = y_test
-  y_pred = pipe.predict(x_test)
+  y_pred = estimator.predict(x_test)
   print(classification_report(y_true,y_pred))
 
-  return pipe,column_names
+  return estimator
 
 def dumping(pipe,column_names):
   # ensure models directory exists and save artifacts
   try:
     os.makedirs("models",exist_ok=True)
-    joblib.dump(pipe, "models/pipe.pkl")
+    joblib.dump(pipe, "models/estimator.pkl")
     joblib.dump(column_names, "models/column_names.pkl")
     print(f"Saved models/pipe.pkl and models/column_names.pkl successfully ✅")
   except Exception as e:
     print(f"Something went wrong while dumping. Message: {e}")
 
 def main():
-  x,y,column_names = data_collection()
-  pipe,clmn_names = model(x,y,column_names)
-  dumping(pipe,clmn_names)
+  x,y = data_collection()
+  column_names = get_column_names()
+  estimator = model(x,y)
+  dumping(estimator,column_names)
 
 if __name__ == "__main__":
   main()
diff --git a/models/column_names.pkl b/models/column_names.pkl
deleted file mode 100644
index c822642..0000000
Binary files a/models/column_names.pkl and /dev/null differ
diff --git a/models/pipe.pkl b/models/pipe.pkl
deleted file mode 100644
index e0950dd..0000000
Binary files a/models/pipe.pkl and /dev/null differ
diff --git a/notebooks/research_LR.ipynb b/notebooks/research_LR.ipynb
deleted file mode 100644
index befd33f..0000000
--- a/notebooks/research_LR.ipynb
+++ /dev/null
@@ -1,399 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "d689398c",
-   "metadata": {},
-   "source": [
-    "# The CosmoClassifier\n",
-    "In this project, I have used the Data Release 18 version of Sloan Digital Sky Survey (SDSS) dataset to train a classifier algorithm to predict whether the given credentials corresponds to a Galaxy(class 0), Star(class 1) or Quasar(class 2). This notebook is used as a playground to test different hyperparameter settings as well as preprocessing approaches. \n",
-    "\n",
-    "We will 4 different classifier algorithms to test the results and select the one which offers the best result. These are:\n",
-    "1. Random Forest \n",
-    "2. Logistic Regression **(This File)**\n",
-    "3. Suppor Vector Classifier (with RBF kerel)\n",
-    "\n",
-    "The models are implemented in separate `.ipynb` files to avoid confusion in one notebook. You can find them all in the `notebooks` subdirectory.\n",
-    "   \n",
-    "We will also use 2 different dimensionality reduction techniques, which include:\n",
-    "1. Linear Discriminant Analysis (LDA)\n",
-    "2. Principle Component Analysis (PCA)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "4347159a",
-   "metadata": {},
-   "source": [
-    "Importing the libraries"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "30c81e59",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from sklearn.linear_model import LogisticRegression\n",
-    "from sklearn.decomposition import PCA\n",
-    "from sklearn.discriminant_analysis import LinearDiscriminantAnalysis as LDA\n",
-    "from sklearn.model_selection import cross_val_score,KFold,train_test_split\n",
-    "from sklearn.preprocessing import StandardScaler\n",
-    "from sklearn.impute import SimpleImputer\n",
-    "from sklearn.pipeline import Pipeline\n",
-    "import numpy as np"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "b6b6be20",
-   "metadata": {},
-   "source": [
-    "## Basic Preprocessing"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "bdcf3bf1",
-   "metadata": {},
-   "source": [
-    "Importing the dataset"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "id": "bbf4bead",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Index(['objid', 'specobjid', 'ra', 'dec', 'u', 'g', 'r', 'i', 'z', 'run',\n",
-       "       'rerun', 'camcol', 'field', 'plate', 'mjd', 'fiberid', 'petroRad_u',\n",
-       "       'petroRad_g', 'petroRad_i', 'petroRad_r', 'petroRad_z', 'petroFlux_u',\n",
-       "       'petroFlux_g', 'petroFlux_i', 'petroFlux_r', 'petroFlux_z',\n",
-       "       'petroR50_u', 'petroR50_g', 'petroR50_i', 'petroR50_r', 'petroR50_z',\n",
-       "       'psfMag_u', 'psfMag_r', 'psfMag_g', 'psfMag_i', 'psfMag_z', 'expAB_u',\n",
-       "       'expAB_g', 'expAB_r', 'expAB_i', 'expAB_z', 'redshift', 'class'],\n",
-       "      dtype='object')"
-      ]
-     },
-     "execution_count": 11,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "import pandas as pd\n",
-    "\n",
-    "df_raw = pd.read_csv(\"Datasets/SDSS_DR18.csv\")\n",
-    "df_raw.columns"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "05c201af",
-   "metadata": {},
-   "source": [
-    "Dropping the identifier columns which may lead to data leakage"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "id": "3376723a",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df_raw = df_raw.drop(columns=[\"objid\", \"specobjid\", \"run\", \"rerun\", \"camcol\", \"field\", \"plate\", \"mjd\", \"fiberid\"])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "e89665e6",
-   "metadata": {},
-   "source": [
-    "Identifying and mapping the classes"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "id": "bc3c7eb9",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "class\n",
-      "GALAXY    52343\n",
-      "STAR      37232\n",
-      "QSO       10425\n",
-      "Name: count, dtype: int64\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(df_raw[\"class\"].value_counts())\n",
-    "df_1 = df_raw.copy()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "id": "323fb64a",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0    0\n",
-       "1    1\n",
-       "2    0\n",
-       "3    1\n",
-       "4    1\n",
-       "5    1\n",
-       "6    1\n",
-       "7    0\n",
-       "8    0\n",
-       "9    1\n",
-       "Name: class, dtype: int64"
-      ]
-     },
-     "execution_count": 14,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df_1[\"class\"] = df_1[\"class\"].map({\n",
-    "  \"GALAXY\":0,\n",
-    "  \"STAR\":1,\n",
-    "  \"QSO\":2\n",
-    "})\n",
-    "df_1[\"class\"].head(10)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "e9fd0660",
-   "metadata": {},
-   "source": [
-    "Checking for null values"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "id": "f3edb7ff",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "ra     dec    u      g      r      i      z      petroRad_u  petroRad_g  petroRad_i  petroRad_r  petroRad_z  petroFlux_u  petroFlux_g  petroFlux_i  petroFlux_r  petroFlux_z  petroR50_u  petroR50_g  petroR50_i  petroR50_r  petroR50_z  psfMag_u  psfMag_r  psfMag_g  psfMag_i  psfMag_z  expAB_u  expAB_g  expAB_r  expAB_i  expAB_z  redshift  class\n",
-       "False  False  False  False  False  False  False  False       False       False       False       False       False        False        False        False        False        False       False       False       False       False       False     False     False     False     False     False    False    False    False    False    False     False    100000\n",
-       "Name: count, dtype: int64"
-      ]
-     },
-     "execution_count": 15,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df_1.isna().value_counts()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "0004f96e",
-   "metadata": {},
-   "source": [
-    "No null values were found, so we are going to skip dropping nulls."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "87cd852f",
-   "metadata": {},
-   "source": [
-    "Copying the dataset and specifying the target & feature columns"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "id": "004ddce4",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df = df_1.copy()\n",
-    "y = df.iloc[:,-1]      # Target Column\n",
-    "x = df.iloc[:,:-1]     # Feature Column"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "52d5323c",
-   "metadata": {},
-   "source": [
-    "## ML Preprocessing, Model training, Evaluation"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "3344718d",
-   "metadata": {},
-   "source": [
-    "Performing train-test split"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "id": "1a8c59cb",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "x_train,x_test,y_train,y_test = train_test_split(x,y,test_size=2/10,random_state=120,shuffle=True,stratify=y)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "26187142",
-   "metadata": {},
-   "source": [
-    "Building Pipeline (PCA)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "id": "64208f23",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[0.97348053 0.97083971 0.97308973]\n",
-      "Average = 0.972469989894595\n"
-     ]
-    }
-   ],
-   "source": [
-    "lr_model = LogisticRegression(\n",
-    "  C=0.001,penalty=\"l2\",solver=\"saga\",\n",
-    "  class_weight=\"balanced\",random_state=191,max_iter=10_000,n_jobs=-1)\n",
-    "pca = PCA(n_components=20,random_state=69)\n",
-    "\n",
-    "preprocessor = Pipeline([\n",
-    "  (\"imputation\",SimpleImputer(strategy=\"median\")),\n",
-    "  (\"scale\", StandardScaler()),\n",
-    "  (\"pca\",pca)\n",
-    "])\n",
-    "pipe = Pipeline([\n",
-    "  (\"preprocessor\",preprocessor),\n",
-    "  (\"model\",lr_model)\n",
-    "])\n",
-    "\n",
-    "kfold = KFold(n_splits=3,shuffle=True,random_state=10)\n",
-    "score = cross_val_score(pipe,x,y,cv=kfold)\n",
-    "print(score)\n",
-    "print(f\"Average = {score.mean()}\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "07541ec8",
-   "metadata": {},
-   "source": [
-    "Building Pipeline (LDA)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 30,
-   "id": "e98d1f0c",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[0.98341033 0.98286983 0.98346983]\n",
-      "Average = 0.983249998396666\n"
-     ]
-    }
-   ],
-   "source": [
-    "lr_model = LogisticRegression(\n",
-    "  C=0.001,penalty=\"l2\",solver=\"saga\",\n",
-    "  class_weight=\"balanced\",random_state=191,max_iter=10_000,n_jobs=-1)\n",
-    "lda = LDA(n_components=2)    \n",
-    "# There are only 2 possible values(1,2) for n_components since there are only 3 classes. \n",
-    "# n_components=2 gave much better score, so I kept that \n",
-    "\n",
-    "preprocessor = Pipeline([\n",
-    "  (\"imputation\",SimpleImputer(strategy=\"median\")),\n",
-    "  (\"scale\", StandardScaler()),\n",
-    "  (\"lda\",lda)\n",
-    "])\n",
-    "pipe = Pipeline([\n",
-    "  (\"preprocessor\",preprocessor),\n",
-    "  (\"model\",lr_model)\n",
-    "])\n",
-    "\n",
-    "kfold = KFold(n_splits=3,shuffle=True,random_state=10)\n",
-    "score = cross_val_score(pipe,x,y,cv=kfold)\n",
-    "print(score)\n",
-    "print(f\"Average = {score.mean()}\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "3ea1afbe",
-   "metadata": {},
-   "source": [
-    "Here is the summary of the entire calculation"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "aaf8ad60",
-   "metadata": {},
-   "source": [
-    "| Dimensionality Reduction / Models $\\downarrow \\leftrightarrow$ | Random Forest | Support Vector Classifier | Logistic Regression |\n",
-    "| :--- | :--- | :--- | :--- |\n",
-    "| Principal Component Analysis | 0.9803 | 0.9471 | 0.9725 |\n",
-    "| Linear Discriminant Analysis | 0.9846 | 0.9832 | 0.9832 |\n",
-    "| Sequential Feature Selection | 0.9870 | Null | Null |"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.13.7"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/notebooks/research_RF.ipynb b/notebooks/research_RF.ipynb
deleted file mode 100644
index 41ac39a..0000000
--- a/notebooks/research_RF.ipynb
+++ /dev/null
@@ -1,1751 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "d689398c",
-   "metadata": {},
-   "source": [
-    "# The CosmoClassifier\n",
-    "In this project, I have used the Data Release 18 version of Sloan Digital Sky Survey (SDSS) dataset to train a classifier algorithm to predict whether the given credentials corresponds to a Galaxy(class 0), Star(class 1) or Quasar(class 2). This notebook is used as a playground to test different hyperparameter settings as well as preprocessing approaches. \n",
-    "\n",
-    "We will 4 different classifier algorithms to test the results and select the one which offers the best result. These are:\n",
-    "1. Random Forest **(This File)**\n",
-    "2. Logistic Regression\n",
-    "3. Suppor Vector Classifier (with RBF kerel)\n",
-    "\n",
-    "The models are implemented in separate `.ipynb` files to avoid confusion in one notebook. You can find them all in the `notebooks` subdirectory.\n",
-    "   \n",
-    "We will also use 3 different dimensionality reduction techniques, which include:\n",
-    "1. Sequential Feature Selection (SFS)\n",
-    "2. Linear Discriminant Analysis (LDA)\n",
-    "3. Principle Component Analysis (PCA)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "4347159a",
-   "metadata": {},
-   "source": [
-    "Importing the libraries"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 43,
-   "id": "30c81e59",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from sklearn.ensemble import RandomForestClassifier\n",
-    "from sklearn.decomposition import PCA\n",
-    "from sklearn.discriminant_analysis import LinearDiscriminantAnalysis as LDA\n",
-    "from sklearn.model_selection import cross_val_score,KFold,train_test_split,learning_curve,validation_curve\n",
-    "from sklearn.preprocessing import StandardScaler\n",
-    "from sklearn.impute import SimpleImputer\n",
-    "from sklearn.feature_selection import SequentialFeatureSelector\n",
-    "from sklearn.pipeline import Pipeline\n",
-    "import numpy as np\n",
-    "import pandas as pd\n",
-    "from matplotlib import pyplot as plt\n",
-    "import seaborn as sns"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "b6b6be20",
-   "metadata": {},
-   "source": [
-    "## Basic Preprocessing"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "bdcf3bf1",
-   "metadata": {},
-   "source": [
-    "Importing the dataset"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "bbf4bead",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df_raw = pd.read_csv(\"Datasets/SDSS_DR18.csv\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "05c201af",
-   "metadata": {},
-   "source": [
-    "Dropping the identifier columns which may lead to data leakage"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "id": "3376723a",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df_raw = df_raw.drop(columns=[\"objid\", \"specobjid\", \"run\", \"rerun\", \"camcol\", \"field\", \"plate\", \"mjd\", \"fiberid\"])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "e89665e6",
-   "metadata": {},
-   "source": [
-    "Identifying and mapping the classes"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "id": "bc3c7eb9",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "class\n",
-      "GALAXY    52343\n",
-      "STAR      37232\n",
-      "QSO       10425\n",
-      "Name: count, dtype: int64\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(df_raw[\"class\"].value_counts())\n",
-    "df_1 = df_raw.copy()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "id": "323fb64a",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0    0\n",
-       "1    1\n",
-       "2    0\n",
-       "3    1\n",
-       "4    1\n",
-       "5    1\n",
-       "6    1\n",
-       "7    0\n",
-       "8    0\n",
-       "9    1\n",
-       "Name: class, dtype: int64"
-      ]
-     },
-     "execution_count": 23,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df_1[\"class\"] = df_1[\"class\"].map({\n",
-    "  \"GALAXY\":0,\n",
-    "  \"STAR\":1,\n",
-    "  \"QSO\":2\n",
-    "})\n",
-    "df_1[\"class\"].head(10)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "e9fd0660",
-   "metadata": {},
-   "source": [
-    "Checking for null values"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "id": "f3edb7ff",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "ra     dec    u      g      r      i      z      petroRad_u  petroRad_g  petroRad_i  petroRad_r  petroRad_z  petroFlux_u  petroFlux_g  petroFlux_i  petroFlux_r  petroFlux_z  petroR50_u  petroR50_g  petroR50_i  petroR50_r  petroR50_z  psfMag_u  psfMag_r  psfMag_g  psfMag_i  psfMag_z  expAB_u  expAB_g  expAB_r  expAB_i  expAB_z  redshift  class\n",
-       "False  False  False  False  False  False  False  False       False       False       False       False       False        False        False        False        False        False       False       False       False       False       False     False     False     False     False     False    False    False    False    False    False     False    100000\n",
-       "Name: count, dtype: int64"
-      ]
-     },
-     "execution_count": 24,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df_1.isna().value_counts()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "0004f96e",
-   "metadata": {},
-   "source": [
-    "No null values were found, so we are going to skip dropping nulls."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "87cd852f",
-   "metadata": {},
-   "source": [
-    "Copying the dataset and specifying the target & feature columns"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 37,
-   "id": "004ddce4",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df = df_1.copy()\n",
-    "column_names = df.columns\n",
-    "y = df.iloc[:,-1].to_numpy()      # Target Column\n",
-    "x = df.iloc[:,:-1]    # Feature Column\n",
-    "feature_names = x.columns\n",
-    "x = x.to_numpy()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "52d5323c",
-   "metadata": {},
-   "source": [
-    "## ML Preprocessing, Model training, Evaluation "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "3344718d",
-   "metadata": {},
-   "source": [
-    "Performing train-test split"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "id": "1a8c59cb",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "x_train,x_test,y_train,y_test = train_test_split(x,y,test_size=2/10,random_state=120,shuffle=True,stratify=y)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "0cfff51a",
-   "metadata": {},
-   "source": [
-    "### Building Pipeline (SFS)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "10fddd1a",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "rf_model = RandomForestClassifier(\n",
-    "  n_estimators=150,max_depth=10,random_state=102,class_weight=\"balanced\",n_jobs=-1)\n",
-    "sfs = SequentialFeatureSelector(\n",
-    "  rf_model,n_features_to_select=\"auto\",tol=0.007,direction=\"forward\",cv=None)\n",
-    "\n",
-    "preprocessor = Pipeline([\n",
-    "  (\"imputation\",SimpleImputer(strategy=\"median\")),\n",
-    "  (\"scale\", StandardScaler()),\n",
-    "  (\"sfs\",sfs)\n",
-    "])\n",
-    "pipe = Pipeline([\n",
-    "  (\"preprocessor\",preprocessor),\n",
-    "  (\"model\",rf_model)\n",
-    "])\n",
-    "\n",
-    "kfold = KFold(n_splits=3,shuffle=True,random_state=10)\n",
-    "score = cross_val_score(pipe,x,y,cv=kfold)\n",
-    "print(score)\n",
-    "print(f\"Average = {score.mean()}\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "26187142",
-   "metadata": {},
-   "source": [
-    "### Building Pipeline (PCA)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 63,
-   "id": "64208f23",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[0.98050039 0.97929979 0.98100981]\n",
-      "Average = 0.980269997696077\n"
-     ]
-    }
-   ],
-   "source": [
-    "rf_model = RandomForestClassifier(\n",
-    "  n_estimators=150,max_depth=10,random_state=103,class_weight=\"balanced\",n_jobs=-1)\n",
-    "pca = PCA(n_components=20,random_state=19)\n",
-    "\n",
-    "preprocessor = Pipeline([\n",
-    "  (\"imputation\",SimpleImputer(strategy=\"median\")),\n",
-    "  (\"scale\", StandardScaler()),\n",
-    "  (\"pca\",pca)\n",
-    "])\n",
-    "pipe = Pipeline([\n",
-    "  (\"preprocessor\",preprocessor),\n",
-    "  (\"model\",rf_model)\n",
-    "])\n",
-    "\n",
-    "kfold = KFold(n_splits=3,shuffle=True,random_state=10)\n",
-    "score = cross_val_score(pipe,x,y,cv=kfold)\n",
-    "print(score)\n",
-    "print(f\"Average = {score.mean()}\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "44a8655b",
-   "metadata": {},
-   "source": [
-    "#### Calculating **loading** dataframe"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 64,
-   "id": "d67a2663",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<style>#sk-container-id-3 {\n",
-       "  /* Definition of color scheme common for light and dark mode */\n",
-       "  --sklearn-color-text: #000;\n",
-       "  --sklearn-color-text-muted: #666;\n",
-       "  --sklearn-color-line: gray;\n",
-       "  /* Definition of color scheme for unfitted estimators */\n",
-       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
-       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
-       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
-       "  --sklearn-color-unfitted-level-3: chocolate;\n",
-       "  /* Definition of color scheme for fitted estimators */\n",
-       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
-       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
-       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
-       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
-       "\n",
-       "  /* Specific color for light theme */\n",
-       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
-       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
-       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
-       "  --sklearn-color-icon: #696969;\n",
-       "\n",
-       "  @media (prefers-color-scheme: dark) {\n",
-       "    /* Redefinition of color scheme for dark theme */\n",
-       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
-       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
-       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
-       "    --sklearn-color-icon: #878787;\n",
-       "  }\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-3 {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-3 pre {\n",
-       "  padding: 0;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-3 input.sk-hidden--visually {\n",
-       "  border: 0;\n",
-       "  clip: rect(1px 1px 1px 1px);\n",
-       "  clip: rect(1px, 1px, 1px, 1px);\n",
-       "  height: 1px;\n",
-       "  margin: -1px;\n",
-       "  overflow: hidden;\n",
-       "  padding: 0;\n",
-       "  position: absolute;\n",
-       "  width: 1px;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-3 div.sk-dashed-wrapped {\n",
-       "  border: 1px dashed var(--sklearn-color-line);\n",
-       "  margin: 0 0.4em 0.5em 0.4em;\n",
-       "  box-sizing: border-box;\n",
-       "  padding-bottom: 0.4em;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-3 div.sk-container {\n",
-       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
-       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
-       "     so we also need the `!important` here to be able to override the\n",
-       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
-       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
-       "  display: inline-block !important;\n",
-       "  position: relative;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-3 div.sk-text-repr-fallback {\n",
-       "  display: none;\n",
-       "}\n",
-       "\n",
-       "div.sk-parallel-item,\n",
-       "div.sk-serial,\n",
-       "div.sk-item {\n",
-       "  /* draw centered vertical line to link estimators */\n",
-       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
-       "  background-size: 2px 100%;\n",
-       "  background-repeat: no-repeat;\n",
-       "  background-position: center center;\n",
-       "}\n",
-       "\n",
-       "/* Parallel-specific style estimator block */\n",
-       "\n",
-       "#sk-container-id-3 div.sk-parallel-item::after {\n",
-       "  content: \"\";\n",
-       "  width: 100%;\n",
-       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
-       "  flex-grow: 1;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-3 div.sk-parallel {\n",
-       "  display: flex;\n",
-       "  align-items: stretch;\n",
-       "  justify-content: center;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "  position: relative;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-3 div.sk-parallel-item {\n",
-       "  display: flex;\n",
-       "  flex-direction: column;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-3 div.sk-parallel-item:first-child::after {\n",
-       "  align-self: flex-end;\n",
-       "  width: 50%;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-3 div.sk-parallel-item:last-child::after {\n",
-       "  align-self: flex-start;\n",
-       "  width: 50%;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-3 div.sk-parallel-item:only-child::after {\n",
-       "  width: 0;\n",
-       "}\n",
-       "\n",
-       "/* Serial-specific style estimator block */\n",
-       "\n",
-       "#sk-container-id-3 div.sk-serial {\n",
-       "  display: flex;\n",
-       "  flex-direction: column;\n",
-       "  align-items: center;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "  padding-right: 1em;\n",
-       "  padding-left: 1em;\n",
-       "}\n",
-       "\n",
-       "\n",
-       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
-       "clickable and can be expanded/collapsed.\n",
-       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
-       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
-       "*/\n",
-       "\n",
-       "/* Pipeline and ColumnTransformer style (default) */\n",
-       "\n",
-       "#sk-container-id-3 div.sk-toggleable {\n",
-       "  /* Default theme specific background. It is overwritten whether we have a\n",
-       "  specific estimator or a Pipeline/ColumnTransformer */\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "}\n",
-       "\n",
-       "/* Toggleable label */\n",
-       "#sk-container-id-3 label.sk-toggleable__label {\n",
-       "  cursor: pointer;\n",
-       "  display: flex;\n",
-       "  width: 100%;\n",
-       "  margin-bottom: 0;\n",
-       "  padding: 0.5em;\n",
-       "  box-sizing: border-box;\n",
-       "  text-align: center;\n",
-       "  align-items: start;\n",
-       "  justify-content: space-between;\n",
-       "  gap: 0.5em;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-3 label.sk-toggleable__label .caption {\n",
-       "  font-size: 0.6rem;\n",
-       "  font-weight: lighter;\n",
-       "  color: var(--sklearn-color-text-muted);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-3 label.sk-toggleable__label-arrow:before {\n",
-       "  /* Arrow on the left of the label */\n",
-       "  content: \"▸\";\n",
-       "  float: left;\n",
-       "  margin-right: 0.25em;\n",
-       "  color: var(--sklearn-color-icon);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "}\n",
-       "\n",
-       "/* Toggleable content - dropdown */\n",
-       "\n",
-       "#sk-container-id-3 div.sk-toggleable__content {\n",
-       "  display: none;\n",
-       "  text-align: left;\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-3 div.sk-toggleable__content.fitted {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-0);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-3 div.sk-toggleable__content pre {\n",
-       "  margin: 0.2em;\n",
-       "  border-radius: 0.25em;\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-3 div.sk-toggleable__content.fitted pre {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-0);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
-       "  /* Expand drop-down */\n",
-       "  display: block;\n",
-       "  width: 100%;\n",
-       "  overflow: visible;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
-       "  content: \"▾\";\n",
-       "}\n",
-       "\n",
-       "/* Pipeline/ColumnTransformer-specific style */\n",
-       "\n",
-       "#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-3 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
-       "  background-color: var(--sklearn-color-fitted-level-2);\n",
-       "}\n",
-       "\n",
-       "/* Estimator-specific style */\n",
-       "\n",
-       "/* Colorize estimator box */\n",
-       "#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-3 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-2);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-3 div.sk-label label.sk-toggleable__label,\n",
-       "#sk-container-id-3 div.sk-label label {\n",
-       "  /* The background is the default theme color */\n",
-       "  color: var(--sklearn-color-text-on-default-background);\n",
-       "}\n",
-       "\n",
-       "/* On hover, darken the color of the background */\n",
-       "#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
-       "}\n",
-       "\n",
-       "/* Label box, darken color on hover, fitted */\n",
-       "#sk-container-id-3 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  background-color: var(--sklearn-color-fitted-level-2);\n",
-       "}\n",
-       "\n",
-       "/* Estimator label */\n",
-       "\n",
-       "#sk-container-id-3 div.sk-label label {\n",
-       "  font-family: monospace;\n",
-       "  font-weight: bold;\n",
-       "  display: inline-block;\n",
-       "  line-height: 1.2em;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-3 div.sk-label-container {\n",
-       "  text-align: center;\n",
-       "}\n",
-       "\n",
-       "/* Estimator-specific */\n",
-       "#sk-container-id-3 div.sk-estimator {\n",
-       "  font-family: monospace;\n",
-       "  border: 1px dotted var(--sklearn-color-border-box);\n",
-       "  border-radius: 0.25em;\n",
-       "  box-sizing: border-box;\n",
-       "  margin-bottom: 0.5em;\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-3 div.sk-estimator.fitted {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-0);\n",
-       "}\n",
-       "\n",
-       "/* on hover */\n",
-       "#sk-container-id-3 div.sk-estimator:hover {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-3 div.sk-estimator.fitted:hover {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-2);\n",
-       "}\n",
-       "\n",
-       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
-       "\n",
-       "/* Common style for \"i\" and \"?\" */\n",
-       "\n",
-       ".sk-estimator-doc-link,\n",
-       "a:link.sk-estimator-doc-link,\n",
-       "a:visited.sk-estimator-doc-link {\n",
-       "  float: right;\n",
-       "  font-size: smaller;\n",
-       "  line-height: 1em;\n",
-       "  font-family: monospace;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "  border-radius: 1em;\n",
-       "  height: 1em;\n",
-       "  width: 1em;\n",
-       "  text-decoration: none !important;\n",
-       "  margin-left: 0.5em;\n",
-       "  text-align: center;\n",
-       "  /* unfitted */\n",
-       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
-       "  color: var(--sklearn-color-unfitted-level-1);\n",
-       "}\n",
-       "\n",
-       ".sk-estimator-doc-link.fitted,\n",
-       "a:link.sk-estimator-doc-link.fitted,\n",
-       "a:visited.sk-estimator-doc-link.fitted {\n",
-       "  /* fitted */\n",
-       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
-       "  color: var(--sklearn-color-fitted-level-1);\n",
-       "}\n",
-       "\n",
-       "/* On hover */\n",
-       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
-       ".sk-estimator-doc-link:hover,\n",
-       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
-       ".sk-estimator-doc-link:hover {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
-       "  color: var(--sklearn-color-background);\n",
-       "  text-decoration: none;\n",
-       "}\n",
-       "\n",
-       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
-       ".sk-estimator-doc-link.fitted:hover,\n",
-       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
-       ".sk-estimator-doc-link.fitted:hover {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-3);\n",
-       "  color: var(--sklearn-color-background);\n",
-       "  text-decoration: none;\n",
-       "}\n",
-       "\n",
-       "/* Span, style for the box shown on hovering the info icon */\n",
-       ".sk-estimator-doc-link span {\n",
-       "  display: none;\n",
-       "  z-index: 9999;\n",
-       "  position: relative;\n",
-       "  font-weight: normal;\n",
-       "  right: .2ex;\n",
-       "  padding: .5ex;\n",
-       "  margin: .5ex;\n",
-       "  width: min-content;\n",
-       "  min-width: 20ex;\n",
-       "  max-width: 50ex;\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  box-shadow: 2pt 2pt 4pt #999;\n",
-       "  /* unfitted */\n",
-       "  background: var(--sklearn-color-unfitted-level-0);\n",
-       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
-       "}\n",
-       "\n",
-       ".sk-estimator-doc-link.fitted span {\n",
-       "  /* fitted */\n",
-       "  background: var(--sklearn-color-fitted-level-0);\n",
-       "  border: var(--sklearn-color-fitted-level-3);\n",
-       "}\n",
-       "\n",
-       ".sk-estimator-doc-link:hover span {\n",
-       "  display: block;\n",
-       "}\n",
-       "\n",
-       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
-       "\n",
-       "#sk-container-id-3 a.estimator_doc_link {\n",
-       "  float: right;\n",
-       "  font-size: 1rem;\n",
-       "  line-height: 1em;\n",
-       "  font-family: monospace;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "  border-radius: 1rem;\n",
-       "  height: 1rem;\n",
-       "  width: 1rem;\n",
-       "  text-decoration: none;\n",
-       "  /* unfitted */\n",
-       "  color: var(--sklearn-color-unfitted-level-1);\n",
-       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-3 a.estimator_doc_link.fitted {\n",
-       "  /* fitted */\n",
-       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
-       "  color: var(--sklearn-color-fitted-level-1);\n",
-       "}\n",
-       "\n",
-       "/* On hover */\n",
-       "#sk-container-id-3 a.estimator_doc_link:hover {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
-       "  color: var(--sklearn-color-background);\n",
-       "  text-decoration: none;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-3 a.estimator_doc_link.fitted:hover {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-3);\n",
-       "}\n",
-       "\n",
-       ".estimator-table summary {\n",
-       "    padding: .5rem;\n",
-       "    font-family: monospace;\n",
-       "    cursor: pointer;\n",
-       "}\n",
-       "\n",
-       ".estimator-table details[open] {\n",
-       "    padding-left: 0.1rem;\n",
-       "    padding-right: 0.1rem;\n",
-       "    padding-bottom: 0.3rem;\n",
-       "}\n",
-       "\n",
-       ".estimator-table .parameters-table {\n",
-       "    margin-left: auto !important;\n",
-       "    margin-right: auto !important;\n",
-       "}\n",
-       "\n",
-       ".estimator-table .parameters-table tr:nth-child(odd) {\n",
-       "    background-color: #fff;\n",
-       "}\n",
-       "\n",
-       ".estimator-table .parameters-table tr:nth-child(even) {\n",
-       "    background-color: #f6f6f6;\n",
-       "}\n",
-       "\n",
-       ".estimator-table .parameters-table tr:hover {\n",
-       "    background-color: #e0e0e0;\n",
-       "}\n",
-       "\n",
-       ".estimator-table table td {\n",
-       "    border: 1px solid rgba(106, 105, 104, 0.232);\n",
-       "}\n",
-       "\n",
-       ".user-set td {\n",
-       "    color:rgb(255, 94, 0);\n",
-       "    text-align: left;\n",
-       "}\n",
-       "\n",
-       ".user-set td.value pre {\n",
-       "    color:rgb(255, 94, 0) !important;\n",
-       "    background-color: transparent !important;\n",
-       "}\n",
-       "\n",
-       ".default td {\n",
-       "    color: black;\n",
-       "    text-align: left;\n",
-       "}\n",
-       "\n",
-       ".user-set td i,\n",
-       ".default td i {\n",
-       "    color: black;\n",
-       "}\n",
-       "\n",
-       ".copy-paste-icon {\n",
-       "    background-image: url(data:image/svg+xml;base64,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);\n",
-       "    background-repeat: no-repeat;\n",
-       "    background-size: 14px 14px;\n",
-       "    background-position: 0;\n",
-       "    display: inline-block;\n",
-       "    width: 14px;\n",
-       "    height: 14px;\n",
-       "    cursor: pointer;\n",
-       "}\n",
-       "</style><body><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;preprocessor&#x27;,\n",
-       "                 Pipeline(steps=[(&#x27;imputation&#x27;,\n",
-       "                                  SimpleImputer(strategy=&#x27;median&#x27;)),\n",
-       "                                 (&#x27;scale&#x27;, StandardScaler()),\n",
-       "                                 (&#x27;pca&#x27;,\n",
-       "                                  PCA(n_components=20, random_state=19))])),\n",
-       "                (&#x27;model&#x27;,\n",
-       "                 RandomForestClassifier(class_weight=&#x27;balanced&#x27;, max_depth=10,\n",
-       "                                        n_estimators=150, n_jobs=-1,\n",
-       "                                        random_state=103))])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-13\" type=\"checkbox\" ><label for=\"sk-estimator-id-13\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>Pipeline</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.7/modules/generated/sklearn.pipeline.Pipeline.html\">?<span>Documentation for Pipeline</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"\">\n",
-       "        <div class=\"estimator-table\">\n",
-       "            <details>\n",
-       "                <summary>Parameters</summary>\n",
-       "                <table class=\"parameters-table\">\n",
-       "                  <tbody>\n",
-       "                    \n",
-       "        <tr class=\"user-set\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('steps',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">steps&nbsp;</td>\n",
-       "            <td class=\"value\">[(&#x27;preprocessor&#x27;, ...), (&#x27;model&#x27;, ...)]</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('transform_input',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">transform_input&nbsp;</td>\n",
-       "            <td class=\"value\">None</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('memory',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">memory&nbsp;</td>\n",
-       "            <td class=\"value\">None</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('verbose',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">verbose&nbsp;</td>\n",
-       "            <td class=\"value\">False</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "                  </tbody>\n",
-       "                </table>\n",
-       "            </details>\n",
-       "        </div>\n",
-       "    </div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-14\" type=\"checkbox\" ><label for=\"sk-estimator-id-14\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>preprocessor: Pipeline</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.7/modules/generated/sklearn.pipeline.Pipeline.html\">?<span>Documentation for preprocessor: Pipeline</span></a></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"preprocessor__\">\n",
-       "        <div class=\"estimator-table\">\n",
-       "            <details>\n",
-       "                <summary>Parameters</summary>\n",
-       "                <table class=\"parameters-table\">\n",
-       "                  <tbody>\n",
-       "                    \n",
-       "        <tr class=\"user-set\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('steps',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">steps&nbsp;</td>\n",
-       "            <td class=\"value\">[(&#x27;imputation&#x27;, ...), (&#x27;scale&#x27;, ...), ...]</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('transform_input',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">transform_input&nbsp;</td>\n",
-       "            <td class=\"value\">None</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('memory',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">memory&nbsp;</td>\n",
-       "            <td class=\"value\">None</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('verbose',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">verbose&nbsp;</td>\n",
-       "            <td class=\"value\">False</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "                  </tbody>\n",
-       "                </table>\n",
-       "            </details>\n",
-       "        </div>\n",
-       "    </div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-15\" type=\"checkbox\" ><label for=\"sk-estimator-id-15\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>SimpleImputer</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.7/modules/generated/sklearn.impute.SimpleImputer.html\">?<span>Documentation for SimpleImputer</span></a></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"preprocessor__imputation__\">\n",
-       "        <div class=\"estimator-table\">\n",
-       "            <details>\n",
-       "                <summary>Parameters</summary>\n",
-       "                <table class=\"parameters-table\">\n",
-       "                  <tbody>\n",
-       "                    \n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('missing_values',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">missing_values&nbsp;</td>\n",
-       "            <td class=\"value\">nan</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"user-set\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('strategy',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">strategy&nbsp;</td>\n",
-       "            <td class=\"value\">&#x27;median&#x27;</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('fill_value',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">fill_value&nbsp;</td>\n",
-       "            <td class=\"value\">None</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('copy',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">copy&nbsp;</td>\n",
-       "            <td class=\"value\">True</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('add_indicator',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">add_indicator&nbsp;</td>\n",
-       "            <td class=\"value\">False</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('keep_empty_features',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">keep_empty_features&nbsp;</td>\n",
-       "            <td class=\"value\">False</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "                  </tbody>\n",
-       "                </table>\n",
-       "            </details>\n",
-       "        </div>\n",
-       "    </div></div></div><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-16\" type=\"checkbox\" ><label for=\"sk-estimator-id-16\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>StandardScaler</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.7/modules/generated/sklearn.preprocessing.StandardScaler.html\">?<span>Documentation for StandardScaler</span></a></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"preprocessor__scale__\">\n",
-       "        <div class=\"estimator-table\">\n",
-       "            <details>\n",
-       "                <summary>Parameters</summary>\n",
-       "                <table class=\"parameters-table\">\n",
-       "                  <tbody>\n",
-       "                    \n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('copy',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">copy&nbsp;</td>\n",
-       "            <td class=\"value\">True</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('with_mean',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">with_mean&nbsp;</td>\n",
-       "            <td class=\"value\">True</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('with_std',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">with_std&nbsp;</td>\n",
-       "            <td class=\"value\">True</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "                  </tbody>\n",
-       "                </table>\n",
-       "            </details>\n",
-       "        </div>\n",
-       "    </div></div></div><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-17\" type=\"checkbox\" ><label for=\"sk-estimator-id-17\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>PCA</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.7/modules/generated/sklearn.decomposition.PCA.html\">?<span>Documentation for PCA</span></a></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"preprocessor__pca__\">\n",
-       "        <div class=\"estimator-table\">\n",
-       "            <details>\n",
-       "                <summary>Parameters</summary>\n",
-       "                <table class=\"parameters-table\">\n",
-       "                  <tbody>\n",
-       "                    \n",
-       "        <tr class=\"user-set\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('n_components',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">n_components&nbsp;</td>\n",
-       "            <td class=\"value\">20</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('copy',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">copy&nbsp;</td>\n",
-       "            <td class=\"value\">True</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('whiten',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">whiten&nbsp;</td>\n",
-       "            <td class=\"value\">False</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('svd_solver',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">svd_solver&nbsp;</td>\n",
-       "            <td class=\"value\">&#x27;auto&#x27;</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('tol',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">tol&nbsp;</td>\n",
-       "            <td class=\"value\">0.0</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('iterated_power',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">iterated_power&nbsp;</td>\n",
-       "            <td class=\"value\">&#x27;auto&#x27;</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('n_oversamples',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">n_oversamples&nbsp;</td>\n",
-       "            <td class=\"value\">10</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('power_iteration_normalizer',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">power_iteration_normalizer&nbsp;</td>\n",
-       "            <td class=\"value\">&#x27;auto&#x27;</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"user-set\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('random_state',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">random_state&nbsp;</td>\n",
-       "            <td class=\"value\">19</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "                  </tbody>\n",
-       "                </table>\n",
-       "            </details>\n",
-       "        </div>\n",
-       "    </div></div></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-18\" type=\"checkbox\" ><label for=\"sk-estimator-id-18\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>RandomForestClassifier</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.7/modules/generated/sklearn.ensemble.RandomForestClassifier.html\">?<span>Documentation for RandomForestClassifier</span></a></div></label><div class=\"sk-toggleable__content fitted\" data-param-prefix=\"model__\">\n",
-       "        <div class=\"estimator-table\">\n",
-       "            <details>\n",
-       "                <summary>Parameters</summary>\n",
-       "                <table class=\"parameters-table\">\n",
-       "                  <tbody>\n",
-       "                    \n",
-       "        <tr class=\"user-set\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('n_estimators',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">n_estimators&nbsp;</td>\n",
-       "            <td class=\"value\">150</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('criterion',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">criterion&nbsp;</td>\n",
-       "            <td class=\"value\">&#x27;gini&#x27;</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"user-set\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('max_depth',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">max_depth&nbsp;</td>\n",
-       "            <td class=\"value\">10</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('min_samples_split',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">min_samples_split&nbsp;</td>\n",
-       "            <td class=\"value\">2</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('min_samples_leaf',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">min_samples_leaf&nbsp;</td>\n",
-       "            <td class=\"value\">1</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('min_weight_fraction_leaf',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">min_weight_fraction_leaf&nbsp;</td>\n",
-       "            <td class=\"value\">0.0</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('max_features',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">max_features&nbsp;</td>\n",
-       "            <td class=\"value\">&#x27;sqrt&#x27;</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('max_leaf_nodes',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">max_leaf_nodes&nbsp;</td>\n",
-       "            <td class=\"value\">None</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('min_impurity_decrease',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">min_impurity_decrease&nbsp;</td>\n",
-       "            <td class=\"value\">0.0</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('bootstrap',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">bootstrap&nbsp;</td>\n",
-       "            <td class=\"value\">True</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('oob_score',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">oob_score&nbsp;</td>\n",
-       "            <td class=\"value\">False</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"user-set\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('n_jobs',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">n_jobs&nbsp;</td>\n",
-       "            <td class=\"value\">-1</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"user-set\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('random_state',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">random_state&nbsp;</td>\n",
-       "            <td class=\"value\">103</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('verbose',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">verbose&nbsp;</td>\n",
-       "            <td class=\"value\">0</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('warm_start',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">warm_start&nbsp;</td>\n",
-       "            <td class=\"value\">False</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"user-set\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('class_weight',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">class_weight&nbsp;</td>\n",
-       "            <td class=\"value\">&#x27;balanced&#x27;</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('ccp_alpha',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">ccp_alpha&nbsp;</td>\n",
-       "            <td class=\"value\">0.0</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('max_samples',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">max_samples&nbsp;</td>\n",
-       "            <td class=\"value\">None</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "\n",
-       "        <tr class=\"default\">\n",
-       "            <td><i class=\"copy-paste-icon\"\n",
-       "                 onclick=\"copyToClipboard('monotonic_cst',\n",
-       "                          this.parentElement.nextElementSibling)\"\n",
-       "            ></i></td>\n",
-       "            <td class=\"param\">monotonic_cst&nbsp;</td>\n",
-       "            <td class=\"value\">None</td>\n",
-       "        </tr>\n",
-       "    \n",
-       "                  </tbody>\n",
-       "                </table>\n",
-       "            </details>\n",
-       "        </div>\n",
-       "    </div></div></div></div></div></div></div><script>function copyToClipboard(text, element) {\n",
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-       "\n",
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-       "</script></body>"
-      ],
-      "text/plain": [
-       "Pipeline(steps=[('preprocessor',\n",
-       "                 Pipeline(steps=[('imputation',\n",
-       "                                  SimpleImputer(strategy='median')),\n",
-       "                                 ('scale', StandardScaler()),\n",
-       "                                 ('pca',\n",
-       "                                  PCA(n_components=20, random_state=19))])),\n",
-       "                ('model',\n",
-       "                 RandomForestClassifier(class_weight='balanced', max_depth=10,\n",
-       "                                        n_estimators=150, n_jobs=-1,\n",
-       "                                        random_state=103))])"
-      ]
-     },
-     "execution_count": 64,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "pipe.fit(x_train,y_train)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 65,
-   "id": "5a184a0c",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(20, 33)"
-      ]
-     },
-     "execution_count": 65,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "after_pca = preprocessor.named_steps[\"pca\"]\n",
-    "comps = after_pca.components_\n",
-    "comps_df = pd.DataFrame(\n",
-    "  comps,index=[f\"PC{i+1}\" for i in range(after_pca.n_components_)], \n",
-    "  columns=feature_names\n",
-    ")\n",
-    "\n",
-    "exp_vars = after_pca.explained_variance_\n",
-    "\n",
-    "def change(x):\n",
-    "  return x * np.sqrt(exp_vars)\n",
-    "loading_df = comps_df.copy()\n",
-    "loading_df = loading_df.apply(change)\n",
-    "loading_df.shape"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "24af0f33",
-   "metadata": {},
-   "source": [
-    "#### PCA Loading heatmap"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 66,
-   "id": "ef8d8351",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1400x900 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.figure(figsize=(14,9))\n",
-    "sns.heatmap(loading_df,cmap=\"icefire\",annot=True,fmt=\".1f\",center=0)\n",
-    "plt.ylabel(\"Principle Components\",fontdict={\"fontsize\":14})\n",
-    "plt.title(\"Heatmap of PCA Loadings\",fontdict={\"fontsize\":19})\n",
-    "plt.tight_layout()\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "23222520",
-   "metadata": {},
-   "source": [
-    "This heatmap visualizes the PCA loadings for 20 Principal Components (PCs) across numerous features. PC1 and PC2 are primarily defined by the spectral bands (g, r, i etc.) and flux values, capturing the bulk of the variance. Loadings quickly drop to near-zero by PC11, indicating that the first 10-12 components are sufficient for dimension reduction and interpretation in this dataset."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "0ee3902a",
-   "metadata": {},
-   "source": [
-    "#### Learning Curve"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "8279807d",
-   "metadata": {},
-   "source": [
-    "Let's plot the **Learning Curve** plot to understand whether the model is fit well or is underfitting/overfitting."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 67,
-   "id": "07b2484b",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "train_size, train_acc, val_acc = learning_curve(\n",
-    "  pipe,x_train,y_train,train_sizes=np.linspace(0.1,1.0,10),\n",
-    "  cv=5,n_jobs=-1,random_state=9,shuffle=True\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 68,
-   "id": "a860e067",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1000x600 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "train_mean = np.mean(train_acc, axis=1)\n",
-    "train_std = np.std(train_acc,axis=1)\n",
-    "val_mean = np.mean(val_acc,axis=1)\n",
-    "val_std = np.std(val_acc,axis=1)\n",
-    "\n",
-    "plt.figure(figsize=(10,6))\n",
-    "plt.plot(train_size, train_mean, color=\"red\",marker=\"s\",markersize=4,label=\"Training Accuracy\")\n",
-    "plt.fill_between(train_size, train_mean + train_std , train_mean - train_std, color=\"red\",alpha=0.3)\n",
-    "\n",
-    "plt.plot(train_size, val_mean, color=\"orange\",marker=\"v\",markersize=4,label=\"Validation Accuracy\")\n",
-    "plt.fill_between(train_size, val_mean + val_std, val_mean - val_std, color=\"orange\",alpha=0.3)\n",
-    "\n",
-    "plt.title(\"Learning Curve (Random Forest with PCA)\",fontdict={\"fontsize\":16})\n",
-    "plt.xlabel(\"Train Size\",fontdict={\"fontsize\":13})\n",
-    "plt.ylabel(\"Accuracy\",fontdict={\"fontsize\":13})\n",
-    "plt.ylim(0.85,1.03)\n",
-    "plt.legend()\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "4cb799b2",
-   "metadata": {},
-   "source": [
-    "\n",
-    "This learning curve for a Random Forest model after applying PCA shows excellent performance, with both Training Accuracy (red) and Validation Accuracy (orange) consistently high, clustering around 0.98. Crucially, the minimal gap between the two curves indicates that the model exhibits low variance and is not significantly overfitting the data, even at smaller training sizes (around 10,000 samples). The validation accuracy appears to have converged, suggesting that further increases in the training set size are unlikely to yield substantial improvements in generalization performance. "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "07541ec8",
-   "metadata": {},
-   "source": [
-    "### Building Pipeline (LDA)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 69,
-   "id": "e98d1f0c",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[0.9844  0.98478]\n",
-      "Average = 0.9845900000000001\n"
-     ]
-    }
-   ],
-   "source": [
-    "rf_model = RandomForestClassifier(\n",
-    "  n_estimators=150,max_depth=10,random_state=104,class_weight=\"balanced\",n_jobs=-1)\n",
-    "lda = LDA(n_components=2)    \n",
-    "# There are only 2 possible values for n_components since there are only 3 classes.  \n",
-    "# n_estimaors=2 gave the best score. So I kept it for the final version\n",
-    "preprocessor = Pipeline([\n",
-    "  (\"imputation\",SimpleImputer(strategy=\"median\")),\n",
-    "  (\"scale\", StandardScaler()),\n",
-    "  (\"lda\",lda)\n",
-    "])\n",
-    "pipe = Pipeline([\n",
-    "  (\"preprocessor\",preprocessor),\n",
-    "  (\"model\",rf_model)\n",
-    "])\n",
-    "\n",
-    "kfold = KFold(n_splits=2,shuffle=True,random_state=10)\n",
-    "score = cross_val_score(pipe,x,y,cv=kfold)\n",
-    "print(score)\n",
-    "print(f\"Average = {score.mean()}\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "2b92f790",
-   "metadata": {},
-   "source": [
-    "#### Learning Curve"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "42a10c0b",
-   "metadata": {},
-   "source": [
-    "Let's plot the **Learning Curve** plot to understand whether the model is fit well or is underfitting/overfitting."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 70,
-   "id": "c2f74dd3",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "train_size, train_acc, val_acc = learning_curve(\n",
-    "  pipe,x_train,y_train,train_sizes=np.linspace(0.1,1.0,10),\n",
-    "  cv=5,n_jobs=-1,random_state=9,shuffle=True\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 71,
-   "id": "0e9368c8",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1000x600 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "train_mean = np.mean(train_acc, axis=1)\n",
-    "train_std = np.std(train_acc,axis=1)\n",
-    "val_mean = np.mean(val_acc,axis=1)\n",
-    "val_std = np.std(val_acc,axis=1)\n",
-    "\n",
-    "plt.figure(figsize=(10,6))\n",
-    "plt.plot(train_size, train_mean, color=\"red\",marker=\"s\",markersize=4,label=\"Training Accuracy\")\n",
-    "plt.fill_between(train_size, train_mean + train_std , train_mean - train_std, color=\"red\",alpha=0.3)\n",
-    "\n",
-    "plt.plot(train_size, val_mean, color=\"orange\",marker=\"v\",markersize=4,label=\"Validation Accuracy\")\n",
-    "plt.fill_between(train_size, val_mean + val_std, val_mean - val_std, color=\"orange\",alpha=0.3)\n",
-    "\n",
-    "plt.title(\"Learning Curve (Random Forest with LDA)\",fontdict={\"fontsize\":16})\n",
-    "plt.xlabel(\"Train Size\",fontdict={\"fontsize\":13})\n",
-    "plt.ylabel(\"Accuracy\",fontdict={\"fontsize\":13})\n",
-    "plt.ylim(0.85,1.03)\n",
-    "plt.legend()\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "5d7ae9fd",
-   "metadata": {},
-   "source": [
-    "This learning curve for a Random Forest model using LDA shows that the model achieves near-perfect Training Accuracy (red line) across all sample sizes, hovering just below 1.00. The Validation Accuracy (orange line) is also excellent, remaining stable just under 0.98 as the Train Size increases. The very small gap between the training and validation curves, combined with the high performance, indicates that this model setup (Random Forest with LDA) is both highly accurate and exhibits very low variance/minimal overfitting on the data. 👍"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "0f3c27c4",
-   "metadata": {},
-   "source": [
-    "## Summary"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "144058b9",
-   "metadata": {},
-   "source": [
-    "Here is a summary of the entire calculation"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "55233ef0",
-   "metadata": {},
-   "source": [
-    "| Dimensionality Reduction / Models $\\downarrow \\leftrightarrow$ | Random Forest | Support Vector Classifier | Logistic Regression |\n",
-    "| :--- | :--- | :--- | :--- |\n",
-    "| Principal Component Analysis | 0.9803 | 0.9471 | 0.9725 |\n",
-    "| Linear Discriminant Analysis | 0.9846 | 0.9832 | 0.9832 |\n",
-    "| Sequential Feature Selection | 0.9870 | Null | Null |"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "5dbd1eb1",
-   "metadata": {},
-   "source": [
-    "From this table, we can see that Random Forest with SFS has achieved the best cross validation score overall. But, we are going to avoid it because of it's extreme complexity and inefficiency. In my Macbook Air M2, it took more than 20 minutes to run even with `n_jobs` set to -1 (all CPU cores are used)."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "81af8214",
-   "metadata": {},
-   "source": [
-    "So, we have fixed the Rnadom Forest model with LDA as our final version for the Web App !\n",
-    "\n",
-    "**BUT**, There is a catch! Check the `research_2.ipynb` file to know about it."
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.13.7"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/notebooks/research_SVC.ipynb b/notebooks/research_SVC.ipynb
deleted file mode 100644
index d466213..0000000
--- a/notebooks/research_SVC.ipynb
+++ /dev/null
@@ -1,403 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "d689398c",
-   "metadata": {},
-   "source": [
-    "# The CosmoClassifier\n",
-    "In this project, I have used the Data Release 18 version of Sloan Digital Sky Survey (SDSS) dataset to train a classifier algorithm to predict whether the given credentials corresponds to a Galaxy(class 0), Star(class 1) or Quasar(class 2). This notebook is used as a playground to test different hyperparameter settings as well as preprocessing approaches. \n",
-    "\n",
-    "We will 4 different classifier algorithms to test the results and select the one which offers the best result. These are:\n",
-    "1. Random Forest \n",
-    "2. Logistic Regression\n",
-    "3. Suppor Vector Classifier (with RBF kerel) **(This File)**\n",
-    "\n",
-    "The models are implemented in separate `.ipynb` files to avoid confusion in one notebook. You can find them all in the `notebooks` subdirectory.\n",
-    "   \n",
-    "We will also use 2 different dimensionality reduction techniques, which include:\n",
-    "1. Linear Discriminant Analysis (LDA)\n",
-    "2. Principle Component Analysis (PCA)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "4347159a",
-   "metadata": {},
-   "source": [
-    "Importing the libraries"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "30c81e59",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from sklearn.svm import SVC\n",
-    "from sklearn.decomposition import PCA\n",
-    "from sklearn.discriminant_analysis import LinearDiscriminantAnalysis as LDA\n",
-    "from sklearn.model_selection import cross_val_score,KFold,train_test_split\n",
-    "from sklearn.preprocessing import StandardScaler\n",
-    "from sklearn.impute import SimpleImputer\n",
-    "from sklearn.pipeline import Pipeline\n",
-    "import numpy as np"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "b6b6be20",
-   "metadata": {},
-   "source": [
-    "## Basic Preprocessing"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "bdcf3bf1",
-   "metadata": {},
-   "source": [
-    "Importing the dataset"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 59,
-   "id": "bbf4bead",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Index(['objid', 'specobjid', 'ra', 'dec', 'u', 'g', 'r', 'i', 'z', 'run',\n",
-       "       'rerun', 'camcol', 'field', 'plate', 'mjd', 'fiberid', 'petroRad_u',\n",
-       "       'petroRad_g', 'petroRad_i', 'petroRad_r', 'petroRad_z', 'petroFlux_u',\n",
-       "       'petroFlux_g', 'petroFlux_i', 'petroFlux_r', 'petroFlux_z',\n",
-       "       'petroR50_u', 'petroR50_g', 'petroR50_i', 'petroR50_r', 'petroR50_z',\n",
-       "       'psfMag_u', 'psfMag_r', 'psfMag_g', 'psfMag_i', 'psfMag_z', 'expAB_u',\n",
-       "       'expAB_g', 'expAB_r', 'expAB_i', 'expAB_z', 'redshift', 'class'],\n",
-       "      dtype='object')"
-      ]
-     },
-     "execution_count": 59,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "import pandas as pd\n",
-    "\n",
-    "df_raw = pd.read_csv(\"Datasets/SDSS_DR18.csv\")\n",
-    "df_raw.columns"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "05c201af",
-   "metadata": {},
-   "source": [
-    "Dropping the identifier columns which may lead to data leakage"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 60,
-   "id": "3376723a",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df_raw = df_raw.drop(columns=[\"objid\", \"specobjid\", \"run\", \"rerun\", \"camcol\", \"field\", \"plate\", \"mjd\", \"fiberid\"])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "e89665e6",
-   "metadata": {},
-   "source": [
-    "Identifying and mapping the classes"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 61,
-   "id": "bc3c7eb9",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "class\n",
-      "GALAXY    52343\n",
-      "STAR      37232\n",
-      "QSO       10425\n",
-      "Name: count, dtype: int64\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(df_raw[\"class\"].value_counts())\n",
-    "df_1 = df_raw.copy()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 62,
-   "id": "323fb64a",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0    0\n",
-       "1    1\n",
-       "2    0\n",
-       "3    1\n",
-       "4    1\n",
-       "5    1\n",
-       "6    1\n",
-       "7    0\n",
-       "8    0\n",
-       "9    1\n",
-       "Name: class, dtype: int64"
-      ]
-     },
-     "execution_count": 62,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df_1[\"class\"] = df_1[\"class\"].map({\n",
-    "  \"GALAXY\":0,\n",
-    "  \"STAR\":1,\n",
-    "  \"QSO\":2\n",
-    "})\n",
-    "df_1[\"class\"].head(10)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "e9fd0660",
-   "metadata": {},
-   "source": [
-    "Checking for null values"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 63,
-   "id": "f3edb7ff",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "ra     dec    u      g      r      i      z      petroRad_u  petroRad_g  petroRad_i  petroRad_r  petroRad_z  petroFlux_u  petroFlux_g  petroFlux_i  petroFlux_r  petroFlux_z  petroR50_u  petroR50_g  petroR50_i  petroR50_r  petroR50_z  psfMag_u  psfMag_r  psfMag_g  psfMag_i  psfMag_z  expAB_u  expAB_g  expAB_r  expAB_i  expAB_z  redshift  class\n",
-       "False  False  False  False  False  False  False  False       False       False       False       False       False        False        False        False        False        False       False       False       False       False       False     False     False     False     False     False    False    False    False    False    False     False    100000\n",
-       "Name: count, dtype: int64"
-      ]
-     },
-     "execution_count": 63,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df_1.isna().value_counts()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "0004f96e",
-   "metadata": {},
-   "source": [
-    "No null values were found, so we are going to skip dropping nulls."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "87cd852f",
-   "metadata": {},
-   "source": [
-    "Copying the dataset and specifying the target & feature columns"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 64,
-   "id": "004ddce4",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df = df_1.copy()\n",
-    "y = df.iloc[:,-1]      # Target Column\n",
-    "x = df.iloc[:,:-1]     # Feature Column"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "52d5323c",
-   "metadata": {},
-   "source": [
-    "## ML Preprocessing, Model training, Evaluation"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "3344718d",
-   "metadata": {},
-   "source": [
-    "Performing train-test split"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 65,
-   "id": "1a8c59cb",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "x_train,x_test,y_train,y_test = train_test_split(x,y,test_size=2/10,random_state=120,shuffle=True,stratify=y)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "26187142",
-   "metadata": {},
-   "source": [
-    "Building Pipeline (PCA)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 66,
-   "id": "64208f23",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[0.94756105 0.94575946 0.94800948]\n",
-      "Average = 0.9471099954894671\n"
-     ]
-    }
-   ],
-   "source": [
-    "svc_model = SVC(C=0.001,kernel=\"rbf\",gamma=\"scale\",random_state=39,class_weight=\"balanced\")\n",
-    "pca = PCA(n_components=20,random_state=19)\n",
-    "\n",
-    "preprocessor = Pipeline([\n",
-    "  (\"imputation\",SimpleImputer(strategy=\"median\")),\n",
-    "  (\"scale\", StandardScaler()),\n",
-    "  (\"pca\",pca)\n",
-    "])\n",
-    "pipe = Pipeline([\n",
-    "  (\"preprocessor\",preprocessor),\n",
-    "  (\"model\",svc_model)\n",
-    "])\n",
-    "\n",
-    "kfold = KFold(n_splits=3,shuffle=True,random_state=21)\n",
-    "score = cross_val_score(pipe,x,y,cv=kfold,n_jobs=-1)\n",
-    "print(score)\n",
-    "print(f\"Average = {score.mean()}\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "07541ec8",
-   "metadata": {},
-   "source": [
-    "Building Pipeline (LDA)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "e98d1f0c",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[0.98311034 0.98349983 0.98298983]\n",
-      "Average = 0.983200000896631\n"
-     ]
-    }
-   ],
-   "source": [
-    "svc_model = SVC(C=0.001,kernel=\"rbf\",gamma=\"scale\",random_state=39,class_weight=\"balanced\")\n",
-    "lda = LDA(n_components=2)\n",
-    "# There are only 2 possible values for n_components since there are only 3 classes.  \n",
-    "# n_estimaors=2 gave the best score. So I kept it for the final version\n",
-    "\n",
-    "preprocessor = Pipeline([\n",
-    "  (\"imputation\",SimpleImputer(strategy=\"median\")),\n",
-    "  (\"scale\", StandardScaler()),\n",
-    "  (\"lda\",lda)\n",
-    "])\n",
-    "pipe = Pipeline([\n",
-    "  (\"preprocessor\",preprocessor),\n",
-    "  (\"model\",svc_model)\n",
-    "])\n",
-    "\n",
-    "kfold = KFold(n_splits=3,shuffle=True,random_state=21)\n",
-    "score = cross_val_score(pipe,x,y,cv=kfold,n_jobs=-1)\n",
-    "print(score)\n",
-    "print(f\"Average = {score.mean()}\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "57423456",
-   "metadata": {},
-   "source": [
-    "## Summary"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "7677d03f",
-   "metadata": {},
-   "source": [
-    "Here is the summary of the entire calculation"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "21690aad",
-   "metadata": {},
-   "source": [
-    "| Dimensionality Reduction / Models $\\downarrow \\leftrightarrow$ | Random Forest | Support Vector Classifier | Logistic Regression |\n",
-    "| :--- | :--- | :--- | :--- |\n",
-    "| Principal Component Analysis | 0.9803 | 0.9471 | 0.9725 |\n",
-    "| Linear Discriminant Analysis | 0.9846 | 0.9832 | 0.9832 |\n",
-    "| Sequential Feature Selection | 0.9870 | Null | Null |"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.13.7"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/research.ipynb b/research.ipynb
new file mode 100644
index 0000000..b11b4b5
--- /dev/null
+++ b/research.ipynb
@@ -0,0 +1,716 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "de5efec3",
+   "metadata": {},
+   "source": [
+    "# The CosmoClassifier\n",
+    "In this project, I have used the Data Release 18 version of Sloan Digital Sky Survey (SDSS) dataset to train a classifier algorithm to predict whether the given credentials corresponds to a Galaxy(class 0), Star(class 1) or Quasar(class 2). This notebook is used as a playground to test different hyperparameter settings as well as preprocessing approaches. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7bfbe970",
+   "metadata": {},
+   "source": [
+    "Importing the libraries"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "id": "65339dfd",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.ensemble import RandomForestClassifier,VotingClassifier\n",
+    "from sklearn.svm import SVC\n",
+    "from sklearn.linear_model import LogisticRegression\n",
+    "from xgboost import XGBClassifier\n",
+    "\n",
+    "from sklearn.decomposition import PCA\n",
+    "from sklearn.discriminant_analysis import LinearDiscriminantAnalysis as LDA\n",
+    "\n",
+    "from sklearn.model_selection import train_test_split,learning_curve,RandomizedSearchCV\n",
+    "from sklearn.preprocessing import StandardScaler\n",
+    "from sklearn.impute import SimpleImputer\n",
+    "from sklearn.metrics import classification_report\n",
+    "\n",
+    "from imblearn.over_sampling import SMOTE\n",
+    "from imblearn.pipeline import Pipeline\n",
+    "\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "from matplotlib import pyplot as plt\n",
+    "import seaborn as sns"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b5462e10",
+   "metadata": {},
+   "source": [
+    "## Basic Preprocessing"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6bd6ed5c",
+   "metadata": {},
+   "source": [
+    "Importing the dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "9251a17f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_raw = pd.read_csv(\"Datasets/SDSS_DR18.csv\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fb7698e5",
+   "metadata": {},
+   "source": [
+    "Dropping the identifier columns which may lead to data leakage"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "e131a247",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_raw = df_raw.drop(columns=[\"objid\", \"specobjid\", \"run\", \"rerun\", \"camcol\", \"field\", \"plate\", \"mjd\", \"fiberid\"])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1c67e5cb",
+   "metadata": {},
+   "source": [
+    "Identifying and mapping the classes"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "9b293d7a",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "class\n",
+      "GALAXY    52343\n",
+      "STAR      37232\n",
+      "QSO       10425\n",
+      "Name: count, dtype: int64\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(df_raw[\"class\"].value_counts())\n",
+    "df_1 = df_raw.copy()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "23334887",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0    0\n",
+       "1    1\n",
+       "2    0\n",
+       "3    1\n",
+       "4    1\n",
+       "5    1\n",
+       "6    1\n",
+       "7    0\n",
+       "8    0\n",
+       "9    1\n",
+       "Name: class, dtype: int64"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_1[\"class\"] = df_1[\"class\"].map({\n",
+    "  \"GALAXY\":0,\n",
+    "  \"STAR\":1,\n",
+    "  \"QSO\":2\n",
+    "})\n",
+    "df_1[\"class\"].head(10)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d38267dc",
+   "metadata": {},
+   "source": [
+    "Checking for null values"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "9fd2f567",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "ra     dec    u      g      r      i      z      petroRad_u  petroRad_g  petroRad_i  petroRad_r  petroRad_z  petroFlux_u  petroFlux_g  petroFlux_i  petroFlux_r  petroFlux_z  petroR50_u  petroR50_g  petroR50_i  petroR50_r  petroR50_z  psfMag_u  psfMag_r  psfMag_g  psfMag_i  psfMag_z  expAB_u  expAB_g  expAB_r  expAB_i  expAB_z  redshift  class\n",
+       "False  False  False  False  False  False  False  False       False       False       False       False       False        False        False        False        False        False       False       False       False       False       False     False     False     False     False     False    False    False    False    False    False     False    100000\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_1.isna().value_counts()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e432ae9d",
+   "metadata": {},
+   "source": [
+    "No null values were found, so we are going to skip dropping nulls."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "601b2631",
+   "metadata": {},
+   "source": [
+    "Copying the dataset and specifying the target & feature columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "c4fac5ad",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df = df_1.copy()\n",
+    "column_names = df.columns\n",
+    "y = df.iloc[:,-1].to_numpy()      # Target Column\n",
+    "x = df.iloc[:,:-1]    # Feature Column\n",
+    "feature_names = x.columns\n",
+    "x = x.to_numpy()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c6798d4c",
+   "metadata": {},
+   "source": [
+    "## ML Preprocessing, Model training, Evaluation "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "067e5131",
+   "metadata": {},
+   "source": [
+    "Performing train-test split"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "id": "8fbd1495",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x_train,x_test,y_train,y_test = train_test_split(x,y,test_size=2/10,random_state=120,shuffle=True,stratify=y)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "307b8992",
+   "metadata": {},
+   "source": [
+    "Defining the pipeline and the model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "id": "c994254c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# RF, SVC, LR, XGB\n",
+    "rf_model = RandomForestClassifier(random_state=40)\n",
+    "svc_model = SVC(random_state=41)\n",
+    "lr_model = LogisticRegression(random_state=42,max_iter=10_000)\n",
+    "xgb_model = XGBClassifier(random_state=43)\n",
+    "\n",
+    "pca = PCA(random_state=44)\n",
+    "lda = LDA(n_components=2)\n",
+    "\n",
+    "pipe = Pipeline([\n",
+    "  (\"impute\",SimpleImputer(strategy=\"median\")),\n",
+    "  (\"scale\",StandardScaler()),\n",
+    "  (\"smote\",SMOTE(random_state=101)),\n",
+    "  (\"dimen\",pca),\n",
+    "  (\"model\",rf_model)\n",
+    "])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "206e4716",
+   "metadata": {},
+   "source": [
+    "Performing Randomized Search CV"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "id": "0d6802af",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/Sakib/.venvs/jupyterfix/lib/python3.13/site-packages/joblib/externals/loky/process_executor.py:782: UserWarning: A worker stopped while some jobs were given to the executor. This can be caused by a too short worker timeout or by a memory leak.\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Best Configuration:\n",
+      "{'model__n_estimators': np.int64(1100), 'model__max_depth': np.int64(13), 'model__learning_rate': 0.1, 'model': XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
+      "              colsample_bylevel=None, colsample_bynode=None,\n",
+      "              colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
+      "              enable_categorical=False, eval_metric=None, feature_types=None,\n",
+      "              feature_weights=None, gamma=None, grow_policy=None,\n",
+      "              importance_type=None, interaction_constraints=None,\n",
+      "              learning_rate=None, max_bin=None, max_cat_threshold=None,\n",
+      "              max_cat_to_onehot=None, max_delta_step=None, max_depth=None,\n",
+      "              max_leaves=None, min_child_weight=None, missing=nan,\n",
+      "              monotone_constraints=None, multi_strategy=None, n_estimators=None,\n",
+      "              n_jobs=None, num_parallel_tree=None, ...), 'dimen__n_components': 20, 'dimen': PCA(random_state=44)}\n",
+      "Best Score = 0.98765\n"
+     ]
+    }
+   ],
+   "source": [
+    "param_list = [\n",
+    "  { # Random Forest, PCA On\n",
+    "    \"model\": [rf_model],\"model__n_estimators\":np.arange(150,650,100),\n",
+    "    \"model__max_depth\":np.arange(7,14,2), \"dimen\" : [pca], \"dimen__n_components\":[18,20,22]\n",
+    "  },\n",
+    "\n",
+    "  # { # SVC, No dimen. reduction\n",
+    "  #   \"model\": [svc_model], \"model__C\":[0.01,0.1,1,10], \"model__kernel\":[\"rbf\"], \"model__gamma\":[0.01,0.1,1,10],\n",
+    "  #   \"dimen\":[\"passthrough\"]\n",
+    "  # },\n",
+    "\n",
+    "  { # Logistic Regression, No dimen. reduction, l1 penalty, `saga` solver\n",
+    "    \"model\": [lr_model], \"model__C\": [0.01,0.1,1,10], \"model__penalty\":[\"l1\"], \"model__solver\":[\"saga\"],\n",
+    "    \"dimen\": [\"passthrough\"]\n",
+    "  },\n",
+    "  { # Logistic Regression, No dimen. reduction, l2 penalty, `lbfgs` solver\n",
+    "    \"model\": [lr_model], \"model__C\": [0.01,0.1,1,10], \"model__penalty\":[\"l2\"], \"model__solver\":[\"lbfgs\"],\n",
+    "    \"dimen\": [\"passthrough\"]\n",
+    "  },\n",
+    "  { # XGBoost, PCA On\n",
+    "    \"dimen\": [pca], \"dimen__n_components\": [18,20,22],\n",
+    "    \"model\": [xgb_model], \"model__n_estimators\" : np.linspace(500,1100,3,dtype=int),\"model__learning_rate\": [0.01,0.1], \"model__max_depth\":np.arange(7,14,3)\n",
+    "  },\n",
+    "  { # XGBoost, LDA On\n",
+    "    \"dimen\": [lda],\n",
+    "    \"model\": [xgb_model], \"model__n_estimators\" : [500,700,900],\"model__learning_rate\": [0.01,0.1], \"model__max_depth\":np.arange(7,14,3)\n",
+    "  }\n",
+    "]\n",
+    "\n",
+    "rscv = RandomizedSearchCV(\n",
+    "  estimator=pipe,param_distributions=param_list,n_iter=8,cv=5,n_jobs=-1,random_state=50,refit=True\n",
+    ")\n",
+    "\n",
+    "rscv.fit(x_train,y_train)\n",
+    "estimator = rscv.best_estimator_\n",
+    "score = rscv.best_score_\n",
+    "config = rscv.best_params_\n",
+    "print(f\"Best Configuration:\\n{config}\")\n",
+    "print(f\"Best Score = {score}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d25cf9eb",
+   "metadata": {},
+   "source": [
+    "As we can see, the **XGBoost** algorithm paired with PCA dominated the randomized search with the best score of **0.9875**, which is a great score overall. Now, let's calculate the **Classification Report** to find out other metrices."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "id": "4a0a63ca",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "           0       0.99      0.99      0.99     10469\n",
+      "           1       0.99      1.00      0.99      7446\n",
+      "           2       0.97      0.96      0.97      2085\n",
+      "\n",
+      "    accuracy                           0.99     20000\n",
+      "   macro avg       0.98      0.98      0.98     20000\n",
+      "weighted avg       0.99      0.99      0.99     20000\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "y_true = y_test\n",
+    "y_pred = rscv.predict(x_test)\n",
+    "print(classification_report(y_true=y_true,y_pred=y_pred))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b590d691",
+   "metadata": {},
+   "source": [
+    "#### PCA Loading heatmap"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "id": "9831f727",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>ra</th>\n",
+       "      <th>dec</th>\n",
+       "      <th>u</th>\n",
+       "      <th>g</th>\n",
+       "      <th>r</th>\n",
+       "      <th>i</th>\n",
+       "      <th>z</th>\n",
+       "      <th>petroRad_u</th>\n",
+       "      <th>petroRad_g</th>\n",
+       "      <th>petroRad_i</th>\n",
+       "      <th>...</th>\n",
+       "      <th>psfMag_r</th>\n",
+       "      <th>psfMag_g</th>\n",
+       "      <th>psfMag_i</th>\n",
+       "      <th>psfMag_z</th>\n",
+       "      <th>expAB_u</th>\n",
+       "      <th>expAB_g</th>\n",
+       "      <th>expAB_r</th>\n",
+       "      <th>expAB_i</th>\n",
+       "      <th>expAB_z</th>\n",
+       "      <th>redshift</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>PC1</th>\n",
+       "      <td>0.015068</td>\n",
+       "      <td>0.064596</td>\n",
+       "      <td>0.757354</td>\n",
+       "      <td>1.032867</td>\n",
+       "      <td>1.027231</td>\n",
+       "      <td>0.044242</td>\n",
+       "      <td>0.030685</td>\n",
+       "      <td>-0.165071</td>\n",
+       "      <td>-0.321211</td>\n",
+       "      <td>-0.315436</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.680507</td>\n",
+       "      <td>0.580676</td>\n",
+       "      <td>0.036669</td>\n",
+       "      <td>0.026449</td>\n",
+       "      <td>-0.182620</td>\n",
+       "      <td>-0.019467</td>\n",
+       "      <td>-0.041092</td>\n",
+       "      <td>0.010513</td>\n",
+       "      <td>0.009936</td>\n",
+       "      <td>0.953631</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>PC2</th>\n",
+       "      <td>0.035250</td>\n",
+       "      <td>-0.005645</td>\n",
+       "      <td>0.286132</td>\n",
+       "      <td>0.051079</td>\n",
+       "      <td>-0.072749</td>\n",
+       "      <td>-0.029934</td>\n",
+       "      <td>-0.029643</td>\n",
+       "      <td>0.366168</td>\n",
+       "      <td>0.655026</td>\n",
+       "      <td>0.642474</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.580908</td>\n",
+       "      <td>0.676651</td>\n",
+       "      <td>-0.006289</td>\n",
+       "      <td>-0.016463</td>\n",
+       "      <td>-0.083846</td>\n",
+       "      <td>-0.252008</td>\n",
+       "      <td>-0.203290</td>\n",
+       "      <td>-0.026108</td>\n",
+       "      <td>-0.025958</td>\n",
+       "      <td>-0.352012</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>PC3</th>\n",
+       "      <td>0.003487</td>\n",
+       "      <td>0.004921</td>\n",
+       "      <td>-0.004796</td>\n",
+       "      <td>-0.009656</td>\n",
+       "      <td>-0.013238</td>\n",
+       "      <td>0.701161</td>\n",
+       "      <td>0.715387</td>\n",
+       "      <td>0.015478</td>\n",
+       "      <td>0.031724</td>\n",
+       "      <td>0.032044</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.014318</td>\n",
+       "      <td>0.017999</td>\n",
+       "      <td>0.702118</td>\n",
+       "      <td>0.715971</td>\n",
+       "      <td>0.000755</td>\n",
+       "      <td>-0.001605</td>\n",
+       "      <td>0.001620</td>\n",
+       "      <td>0.702053</td>\n",
+       "      <td>0.715878</td>\n",
+       "      <td>-0.006495</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>3 rows × 33 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "           ra       dec         u         g         r         i         z  \\\n",
+       "PC1  0.015068  0.064596  0.757354  1.032867  1.027231  0.044242  0.030685   \n",
+       "PC2  0.035250 -0.005645  0.286132  0.051079 -0.072749 -0.029934 -0.029643   \n",
+       "PC3  0.003487  0.004921 -0.004796 -0.009656 -0.013238  0.701161  0.715387   \n",
+       "\n",
+       "     petroRad_u  petroRad_g  petroRad_i  ...  psfMag_r  psfMag_g  psfMag_i  \\\n",
+       "PC1   -0.165071   -0.321211   -0.315436  ...  0.680507  0.580676  0.036669   \n",
+       "PC2    0.366168    0.655026    0.642474  ...  0.580908  0.676651 -0.006289   \n",
+       "PC3    0.015478    0.031724    0.032044  ...  0.014318  0.017999  0.702118   \n",
+       "\n",
+       "     psfMag_z   expAB_u   expAB_g   expAB_r   expAB_i   expAB_z  redshift  \n",
+       "PC1  0.026449 -0.182620 -0.019467 -0.041092  0.010513  0.009936  0.953631  \n",
+       "PC2 -0.016463 -0.083846 -0.252008 -0.203290 -0.026108 -0.025958 -0.352012  \n",
+       "PC3  0.715971  0.000755 -0.001605  0.001620  0.702053  0.715878 -0.006495  \n",
+       "\n",
+       "[3 rows x 33 columns]"
+      ]
+     },
+     "execution_count": 45,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pca = estimator.named_steps[\"dimen\"]\n",
+    "comp_df = pd.DataFrame(pca.components_,index=[f\"PC{i+1}\" for i in range(pca.n_components_)],columns=feature_names)\n",
+    "exp_var = pca.explained_variance_\n",
+    "\n",
+    "def mod(x):\n",
+    "  return x * np.sqrt(exp_var)\n",
+    "loading_df = comp_df.copy().apply(mod)\n",
+    "loading_df.head(3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "id": "b791bc15",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1400x900 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(14,9))\n",
+    "sns.heatmap(loading_df,cmap=\"icefire\",annot=True,fmt=\".1f\",center=0)\n",
+    "plt.ylabel(\"Principle Components\",fontdict={\"fontsize\":14})\n",
+    "plt.title(\"Heatmap of PCA Loadings\",fontdict={\"fontsize\":19})\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e70bce75",
+   "metadata": {},
+   "source": [
+    "This heatmap visualizes the PCA loadings for 20 Principal Components (PCs) across numerous features. PC1 and PC2 are primarily defined by the spectral bands (g, r, i etc.) and flux values, capturing the bulk of the variance. Loadings quickly drop to near-zero by PC11, indicating that the first 10-12 components are sufficient for dimension reduction and interpretation in this dataset."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cec67ea5",
+   "metadata": {},
+   "source": [
+    "#### Learning Curve"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "607f8b25",
+   "metadata": {},
+   "source": [
+    "Let's plot the **Learning Curve** plot to understand whether the model is fit well or is underfitting/overfitting."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "id": "0e08d100",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "train_size, train_acc, val_acc = learning_curve(\n",
+    "  estimator,x_train,y_train,train_sizes=np.linspace(0.1,1.0,10),\n",
+    "  cv=5,n_jobs=-1,random_state=9,shuffle=True\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "id": "cc187a6c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "train_mean = np.mean(train_acc, axis=1)\n",
+    "train_std = np.std(train_acc,axis=1)\n",
+    "val_mean = np.mean(val_acc,axis=1)\n",
+    "val_std = np.std(val_acc,axis=1)\n",
+    "\n",
+    "plt.figure(figsize=(10,6))\n",
+    "plt.plot(train_size, train_mean, color=\"red\",marker=\"s\",markersize=4,label=\"Training Accuracy\")\n",
+    "plt.fill_between(train_size, train_mean + train_std , train_mean - train_std, color=\"red\",alpha=0.3)\n",
+    "\n",
+    "plt.plot(train_size, val_mean, color=\"orange\",marker=\"v\",markersize=4,label=\"Validation Accuracy\")\n",
+    "plt.fill_between(train_size, val_mean + val_std, val_mean - val_std, color=\"orange\",alpha=0.3)\n",
+    "\n",
+    "plt.title(\"Learning Curve (Random Forest with PCA)\",fontdict={\"fontsize\":16})\n",
+    "plt.xlabel(\"Train Size\",fontdict={\"fontsize\":13})\n",
+    "plt.ylabel(\"Accuracy\",fontdict={\"fontsize\":13})\n",
+    "plt.ylim(0.85,1.03)\n",
+    "plt.legend()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c4e3ce97",
+   "metadata": {},
+   "source": [
+    "\n",
+    "This learning curve for a Random Forest model after applying PCA shows excellent performance, with both Training Accuracy (red) and Validation Accuracy (orange) consistently high, clustering around 0.98. Crucially, the minimal gap between the two curves indicates that the model exhibits low variance and is not significantly overfitting the data, even at smaller training sizes (around 10,000 samples). The validation accuracy appears to have converged, suggesting that further increases in the training set size are unlikely to yield substantial improvements in generalization performance. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "22e6e53d",
+   "metadata": {},
+   "source": [
+    "## Summary\n",
+    "In this notebook, we have performed proper tests to develop the best possible model for our classification task. We will take the code from here to built `fit.py` script from the ground up.\n",
+    "\n",
+    "Check out `research_2.ipynb` file to see the feature reductions I tried out. It is a crucial step to make the app realistic and actually useful."
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "JupyterFix (Py3.13)",
+   "language": "python",
+   "name": "jupyterfix"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.13.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/notebooks/research_2.ipynb b/research_2.ipynb
similarity index 74%
rename from notebooks/research_2.ipynb
rename to research_2.ipynb
index 5d04a29..267f141 100644
--- a/notebooks/research_2.ipynb
+++ b/research_2.ipynb
@@ -38,21 +38,31 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 74,
+   "execution_count": 4,
    "id": "75b129e8",
    "metadata": {},
    "outputs": [],
    "source": [
-    "from sklearn.ensemble import RandomForestClassifier\n",
+    "from sklearn.ensemble import RandomForestClassifier,VotingClassifier\n",
+    "from sklearn.svm import SVC\n",
+    "from sklearn.linear_model import LogisticRegression\n",
+    "from xgboost import XGBClassifier\n",
+    "\n",
     "from sklearn.decomposition import PCA\n",
     "from sklearn.discriminant_analysis import LinearDiscriminantAnalysis as LDA\n",
-    "from sklearn.model_selection import cross_val_score,KFold,train_test_split\n",
+    "\n",
+    "from sklearn.model_selection import train_test_split,learning_curve,RandomizedSearchCV\n",
     "from sklearn.preprocessing import StandardScaler\n",
     "from sklearn.impute import SimpleImputer\n",
-    "from sklearn.feature_selection import SequentialFeatureSelector\n",
-    "from sklearn.pipeline import Pipeline\n",
+    "from sklearn.metrics import classification_report\n",
+    "\n",
+    "from imblearn.over_sampling import SMOTE\n",
+    "from imblearn.pipeline import Pipeline\n",
+    "\n",
     "import numpy as np\n",
-    "import pandas as pd"
+    "import pandas as pd\n",
+    "from matplotlib import pyplot as plt\n",
+    "import seaborn as sns"
    ]
   },
   {
@@ -73,7 +83,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 75,
+   "execution_count": 5,
    "id": "077333af",
    "metadata": {},
    "outputs": [
@@ -90,7 +100,7 @@
        "      dtype='object')"
       ]
      },
-     "execution_count": 75,
+     "execution_count": 5,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -110,7 +120,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 76,
+   "execution_count": 6,
    "id": "7df73004",
    "metadata": {},
    "outputs": [
@@ -126,7 +136,7 @@
        "      dtype='object')"
       ]
      },
-     "execution_count": 76,
+     "execution_count": 6,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -146,7 +156,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 77,
+   "execution_count": 7,
    "id": "400a682c",
    "metadata": {},
    "outputs": [
@@ -169,7 +179,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 78,
+   "execution_count": 8,
    "id": "bb77622d",
    "metadata": {},
    "outputs": [
@@ -189,7 +199,7 @@
        "Name: class, dtype: int64"
       ]
      },
-     "execution_count": 78,
+     "execution_count": 8,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -221,7 +231,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 79,
+   "execution_count": 9,
    "id": "7bcc8335",
    "metadata": {},
    "outputs": [],
@@ -239,7 +249,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 80,
+   "execution_count": 10,
    "id": "c5088740",
    "metadata": {},
    "outputs": [],
@@ -260,7 +270,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 81,
+   "execution_count": 11,
    "id": "3f0a5b5e",
    "metadata": {},
    "outputs": [
@@ -350,7 +360,7 @@
        "1  20.59599  20.34491      1   -0.32593   -0.42295   -0.31765    0.06614  "
       ]
      },
-     "execution_count": 81,
+     "execution_count": 11,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -361,7 +371,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 82,
+   "execution_count": 12,
    "id": "7730e27f",
    "metadata": {},
    "outputs": [],
@@ -371,7 +381,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 83,
+   "execution_count": 13,
    "id": "0d8ea655",
    "metadata": {},
    "outputs": [
@@ -446,7 +456,7 @@
        "1   -0.31765    0.06614  "
       ]
      },
-     "execution_count": 83,
+     "execution_count": 13,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -465,7 +475,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 84,
+   "execution_count": 14,
    "id": "f3d1a893",
    "metadata": {},
    "outputs": [],
@@ -476,7 +486,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 85,
+   "execution_count": 15,
    "id": "8a068db7",
    "metadata": {},
    "outputs": [
@@ -565,7 +575,7 @@
        "2    0.29830      0  "
       ]
      },
-     "execution_count": 85,
+     "execution_count": 15,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -584,7 +594,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 86,
+   "execution_count": 16,
    "id": "46fabf27",
    "metadata": {},
    "outputs": [
@@ -596,7 +606,7 @@
        "      dtype='object')"
       ]
      },
-     "execution_count": 86,
+     "execution_count": 16,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -628,7 +638,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 87,
+   "execution_count": 17,
    "id": "ee0761ba",
    "metadata": {},
    "outputs": [],
@@ -639,182 +649,184 @@
   },
   {
    "cell_type": "markdown",
-   "id": "09909f26",
+   "id": "c936810f",
    "metadata": {},
    "source": [
-    "Building Pipeline (PCA)"
+    "Defining the pipeline and the model"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 88,
-   "id": "30c9e74a",
+   "execution_count": 18,
+   "id": "f1dfdb0c",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[0.96295 0.9646  0.96155 0.97585 0.96245]\n",
-      "Average = 0.96548\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
-    "rf_model = RandomForestClassifier(\n",
-    "  n_estimators=150,max_depth=10,random_state=103,class_weight=\"balanced\",n_jobs=-1)\n",
-    "pca = PCA(n_components=8,random_state=19)\n",
+    "# RF, SVC, LR, XGB\n",
+    "rf_model = RandomForestClassifier(random_state=40)\n",
+    "svc_model = SVC(random_state=41)\n",
+    "lr_model = LogisticRegression(random_state=42,max_iter=10_000)\n",
+    "xgb_model = XGBClassifier(random_state=43)\n",
+    "\n",
+    "pca = PCA(random_state=44)\n",
+    "lda = LDA(n_components=2)\n",
     "\n",
-    "preprocessor = Pipeline([\n",
-    "  (\"imputation\",SimpleImputer(strategy=\"median\")),\n",
-    "  (\"scale\", StandardScaler()),\n",
-    "  (\"pca\",pca)\n",
-    "])\n",
     "pipe = Pipeline([\n",
-    "  (\"preprocessor\",preprocessor),\n",
+    "  (\"impute\",SimpleImputer(strategy=\"median\")),\n",
+    "  (\"scale\",StandardScaler()),\n",
+    "  (\"smote\",SMOTE(random_state=101)),\n",
+    "  (\"dimen\",pca),\n",
     "  (\"model\",rf_model)\n",
-    "])\n",
-    "\n",
-    "kfold = KFold(n_splits=5,shuffle=True,random_state=10)\n",
-    "score = cross_val_score(pipe,x,y,cv=kfold)\n",
-    "print(score)\n",
-    "print(f\"Average = {score.mean()}\")"
+    "])"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "d21b9625",
+   "id": "f10e44ca",
    "metadata": {},
    "source": [
-    "Building Pipeline (LDA)"
+    "Performing Randomized Search CV"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 89,
-   "id": "1709282a",
+   "execution_count": 19,
+   "id": "a729e0dc",
    "metadata": {},
    "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/Sakib/.venvs/jupyterfix/lib/python3.13/site-packages/joblib/externals/loky/process_executor.py:782: UserWarning: A worker stopped while some jobs were given to the executor. This can be caused by a too short worker timeout or by a memory leak.\n",
+      "  warnings.warn(\n"
+     ]
+    },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[0.95758085 0.95550956 0.96399964]\n",
-      "Average = 0.9590300144916611\n"
+      "Best Configuration:\n",
+      "{'model__solver': 'saga', 'model__penalty': 'l1', 'model__C': 10, 'model': LogisticRegression(max_iter=10000, random_state=42), 'dimen': 'passthrough'}\n",
+      "Best Score = 0.9758125\n"
      ]
     }
    ],
    "source": [
-    "rf_model = RandomForestClassifier(\n",
-    "  n_estimators=150,max_depth=10,random_state=104,class_weight=\"balanced\",n_jobs=-1)\n",
-    "lda = LDA(n_components=2)    \n",
-    "# There are only 2 possible values for n_components since there are only 3 classes.  \n",
-    "# n_estimaors=2 gave the best score. So I kept it for the final version\n",
-    "preprocessor = Pipeline([\n",
-    "  (\"imputation\",SimpleImputer(strategy=\"median\")),\n",
-    "  (\"scale\", StandardScaler()),\n",
-    "  (\"lda\",lda)\n",
-    "])\n",
-    "pipe = Pipeline([\n",
-    "  (\"preprocessor\",preprocessor),\n",
-    "  (\"model\",rf_model)\n",
-    "])\n",
+    "param_list = [\n",
+    "  { # Random Forest, PCA On\n",
+    "    \"model\": [rf_model],\"model__n_estimators\":np.arange(150,650,100),\n",
+    "    \"model__max_depth\":np.arange(7,14,2), \"dimen\" : [pca], \"dimen__n_components\": np.arange(5,8,1)\n",
+    "  },\n",
+    "\n",
+    "  # { # SVC, No dimen. reduction\n",
+    "  #   \"model\": [svc_model], \"model__C\":[0.01,0.1,1,10], \"model__kernel\":[\"rbf\"], \"model__gamma\":[0.01,0.1,1,10],\n",
+    "  #   \"dimen\":[\"passthrough\"]\n",
+    "  # },\n",
+    "\n",
+    "  { # Logistic Regression, No dimen. reduction, l1 penalty, `saga` solver\n",
+    "    \"model\": [lr_model], \"model__C\": [0.01,0.1,1,10], \"model__penalty\":[\"l1\"], \"model__solver\":[\"saga\"],\n",
+    "    \"dimen\": [\"passthrough\"]\n",
+    "  },\n",
+    "  { # Logistic Regression, No dimen. reduction, l2 penalty, `lbfgs` solver\n",
+    "    \"model\": [lr_model], \"model__C\": [0.01,0.1,1,10], \"model__penalty\":[\"l2\"], \"model__solver\":[\"lbfgs\"],\n",
+    "    \"dimen\": [\"passthrough\"]\n",
+    "  },\n",
+    "  { # XGBoost, PCA On\n",
+    "    \"dimen\": [pca], \"dimen__n_components\": np.arange(5,8,1),\n",
+    "    \"model\": [xgb_model], \"model__n_estimators\" : np.linspace(500,1100,3,dtype=int),\"model__learning_rate\": [0.01,0.1], \"model__max_depth\":np.arange(7,14,3)\n",
+    "  },\n",
+    "  { # XGBoost, LDA On\n",
+    "    \"dimen\": [lda],\n",
+    "    \"model\": [xgb_model], \"model__n_estimators\" : [500,700,900],\"model__learning_rate\": [0.01,0.1], \"model__max_depth\":np.arange(7,14,3)\n",
+    "  },\n",
+    "  { # XGBoost, No dimen. reduction\n",
+    "    \"dimen\": [\"passthrough\"],\n",
+    "    \"model\": [xgb_model], \"model__n_estimators\" : [500,700,900],\"model__learning_rate\": [0.01,0.1], \"model__max_depth\":np.arange(7,14,3)\n",
+    "  }\n",
+    "]\n",
+    "\n",
+    "rscv = RandomizedSearchCV(\n",
+    "  estimator=pipe,param_distributions=param_list,n_iter=8,cv=5,n_jobs=-1,random_state=50,refit=True\n",
+    ")\n",
     "\n",
-    "kfold = KFold(n_splits=3,shuffle=True,random_state=10)\n",
-    "score = cross_val_score(pipe,x,y,cv=kfold)\n",
-    "print(score)\n",
-    "print(f\"Average = {score.mean()}\")"
+    "rscv.fit(x_train,y_train)\n",
+    "estimator = rscv.best_estimator_\n",
+    "score = rscv.best_score_\n",
+    "config = rscv.best_params_\n",
+    "print(f\"Best Configuration:\\n{config}\")\n",
+    "print(f\"Best Score = {score}\")"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "a3348f29",
+   "id": "cff385ef",
    "metadata": {},
    "source": [
-    "Building Pipeline (SFS)"
+    "Now, let's calculate the **Classification Report**"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 90,
-   "id": "83e5cade",
+   "execution_count": 20,
+   "id": "7d46f1f5",
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[0.98716026 0.98649986 0.98745987]\n",
-      "Average = 0.9870399987974201\n"
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "           0       0.99      0.96      0.98     10469\n",
+      "           1       0.96      1.00      0.98      7446\n",
+      "           2       0.96      0.96      0.96      2085\n",
+      "\n",
+      "    accuracy                           0.97     20000\n",
+      "   macro avg       0.97      0.97      0.97     20000\n",
+      "weighted avg       0.98      0.97      0.97     20000\n",
+      "\n"
      ]
     }
    ],
    "source": [
-    "rf_model = RandomForestClassifier(\n",
-    "  n_estimators=150,max_depth=10,random_state=102,class_weight=\"balanced\",n_jobs=-1)\n",
-    "sfs = SequentialFeatureSelector(\n",
-    "  rf_model,n_features_to_select=\"auto\",tol=0.007,direction=\"forward\",cv=None)\n",
-    "\n",
-    "preprocessor = Pipeline([\n",
-    "  (\"imputation\",SimpleImputer(strategy=\"median\")),\n",
-    "  (\"scale\", StandardScaler()),\n",
-    "  (\"sfs\",sfs)\n",
-    "])\n",
-    "pipe = Pipeline([\n",
-    "  (\"preprocessor\",preprocessor),\n",
-    "  (\"model\",rf_model)\n",
-    "])\n",
-    "\n",
-    "kfold = KFold(n_splits=3,shuffle=True,random_state=10)\n",
-    "score = cross_val_score(pipe,x,y,cv=kfold)\n",
-    "print(score)\n",
-    "print(f\"Average = {score.mean()}\")"
+    "y_true = y_test\n",
+    "y_pred = rscv.predict(x_test)\n",
+    "print(classification_report(y_true=y_true,y_pred=y_pred))"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "005924f6",
+   "id": "ffed0a9f",
    "metadata": {},
    "source": [
-    "## Summary\n",
-    "By performing the feature reduction, I have successfully reduced the number of features from 32 to just 9. Obviously, this reduction comes at a cost. I tested with LDA,PCA and SFS and got the following result;\n",
-    "\n",
-    "| Dimensionality Reduction $\\Rightarrow$ | Principal Component Analysis | Linear Discriminant Analysis | Sequential Feature Selection |\n",
-    "| :--- | :---: | :---: | :---: |\n",
-    "| Before feature reduction | 0.9803 | 0.9846 | 0.9870 |\n",
-    "| After feature reduction | 0.9655 | 0.9590 | 0.9870 |"
+    "As we can see, the overall accuracy decreased about 1% from before. But it is a huge win considering the simplicity we have successfully added by performing the feature reduction."
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "16ea811a",
-   "metadata": {},
-   "source": [
-    "As you can see, almost all the scores decreased by around 0.2. However, SFS managed to keep the scores absolutely the same as before. This is because SFS is a feature selection method, not feature extraction. It eliminates feature columns but does not change the individual data. "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "1c5c530d",
+   "id": "005924f6",
    "metadata": {},
    "source": [
-    "Because of the reduced dimension, the runtime of SFS implementation also dratically reduced. "
+    "## Summary"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "0f3c4d55",
+   "id": "dcaee361",
    "metadata": {},
    "source": [
-    "So, we are going to use the RandomForest implementation with SFS dimensionality reduction in the main web app."
+    "In this notebook, we have explored how we can make the model suitable for user usage by reducing the number of features. We have seen that it has made the model a lot simplier by only sacrificing 1% accuracy. \n",
+    "\n",
+    "We will use the code from this notebook to write `fit.py` from the ground up. "
    ]
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "JupyterFix (Py3.13)",
    "language": "python",
-   "name": "python3"
+   "name": "jupyterfix"
   },
   "language_info": {
    "codemirror_mode": {