Stock Market Forcasting using ARIMA Model on Nifty 50 Data:

I made this project using the time series analysis and forcasting on Nifty50 stock market data using the ARIMA (AutoRegressive Integrated Moving Average) model.

Project Overview:

1. The goal of the project is to understand and forecast stock price movements using classical time series techniques.
2. I used the Nifty 50 index dataset from Kaggle, then cleaned and visualized it, and then applied the ARIMA model for predicting future stock values.

Parameters and the Key Features of the Project:

1. Time Series Plotting and Trend Analysis
2. Decomposition of Time Series (trend, seasonality, residuals)
3. ARIMA (AutoRegressive Integrated Moving Average) Modeling
4. Parameter Tuning using ACF and PACF plots
5. Forecasting future stock values
6. Evaluation using RMSE and visualization

Dataset Source:

I Downloaded this Nifty50 stock dataset from Kaggle.

Libraries, Tools & Technologies I used:

Python, Jupyter Notebook Pandas, NumPy Matplotlib, Seaborn Statsmodels ARIMA modeling