📈 Stock Price Predictor

Welcome to the Stock Price Predictor project! This repository contains a comprehensive Jupyter Notebook that demonstrates how to use various machine learning and deep learning models to forecast stock prices based on historical data.

🚀 Features

• Data Collection: Automatically downloads historical stock data using Yahoo Finance via yfinance.
• Preprocessing: Cleans, normalizes, and prepares data for modeling.
• Visualization: Plots raw and processed data for quick insights.
• Modeling: Implements and compares 12+ models, including:
  • Linear Regression
  • LSTM (Basic & Improved)
  • CNN
  • GRU
  • RNN
  • XGBoost
  • SVR
  • Transformer
  • Random Forest
  • LightGBM
  • Gradient Boosting
  • MLP
  • ElasticNet, Ridge, Lasso
• Performance Comparison: Summarizes and visualizes model performance (MSE, RMSE) to help select the best predictor.
• Robustness Testing: Evaluates models on unseen data.

📝 How to Use

1. Clone the repository:

   git clone https://github.com/yourusername/stock-predictor.git
   cd stock-predictor

2. Install dependencies:

   pip install -r requirements.txt

3. Run the notebook:

   • Open Stock_Price_Predictor.ipynb in Jupyter Notebook or VS Code.
   • Execute cells step by step to follow the workflow.

📊 Results

• The notebook outputs a comparison table and visualizations for all models.
• The best performing model is highlighted based on test MSE/RMSE.

📂 Project Structure

stock-predictor/
├── Stock_Price_Predictor.ipynb
├── preprocess_data.py
├── lstm.py
├── LinearRegressionModel.py
├── stock_data.py
├── visualize.py
├── data_visualization_*.png
├── google.csv / google_preprocessed.csv / googl.csv
├── LICENSE
└── requirements.txt

🏆 Model Selection

At the end of the notebook, you’ll find a summary of all models and a clear indication of which model gave the best prediction for your dataset.

📚 License

This project is licensed under the Apache 2.0 License.

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**Made with ❤️ for learning and