Stock Price Prediction

This project predicts stock prices using a combination of Linear Regression and advanced modeling techniques (Random Forest). It integrates multiple stocks and technical indicators for a comprehensive analysis.

Features

• Supports multiple stock tickers (e.g., AAPL, TSLA, BRK.B, etc.).
• Calculates advanced indicators such as RSI and Bollinger Bands.
• Implements Linear Regression and Random Forest models.
• Visualizes actual vs. predicted prices.

Requirements

• Python 3.7+
• Libraries:
  • pandas
  • numpy
  • matplotlib
  • seaborn
  • alpha_vantage
  • ta
  • scikit-learn

Usage

1. Clone the repository:

   git clone https://github.com/your-username/stock-price-prediction.git
   

Install Dependencies

pip install -r requirements.txt

Run the Script

python stock_prediction.py

Sample Results

Model Performance: Random Forest Mean Squared Error (MSE): 83.4341 Random Forest R²: 0.9999

Visualization:

License

This project is licensed under the MIT License.