Stock Price Prediction Web App

Overview

This project is a Stock Price Prediction Web App built using Streamlit. The application allows users to select a stock from a predefined list, view historical stock data, and visualize actual vs. predicted prices. The model uses machine learning techniques to predict future stock prices and provides technical indicators like SMA, EMA, and Bollinger Bands.

Features

• Stock Selection: Choose from a list of stocks like AAPL, GOOGL, AMZN, TSLA, etc.
• Data Visualization: Displays historical stock prices.
• Stock Price Prediction: Uses an LSTM model to predict future stock prices.
• Performance Metrics: Displays RMSE and R² scores to evaluate model accuracy.
• Technical Indicators:
  • Simple Moving Average (SMA)
  • Exponential Moving Average (EMA)
  • Bollinger Bands

Disclaimer

This stock prediction model is developed for educational purposes only. Predictions generated by this model are not financial advice. Stock markets are influenced by many factors, and no model can guarantee future performance.

Installation

To run the project locally, follow these steps:

1. Clone the repository:

   git clone https://github.com/Elango001/sm/

2. Install dependencies:

   pip install -r requirements.txt

3. Run the application:

   streamlit run main.py

Usage

1. Select a stock from the dropdown menu.
2. View the loaded historical data.
3. See actual vs. predicted stock prices.
4. Analyze model performance (RMSE & R² score).
5. View stock indicators (SMA, EMA, Bollinger Bands).
6. Predict future stock prices for the next 10 days.

Model Details

• The application loads stock data using MODEL.py.
• Data is preprocessed by extracting features and normalizing values.
• A machine learning model is trained using LSTM (Long Short-Term Memory) networks.
• The model is evaluated using RMSE and R² scores.
• Predictions for future prices are displayed graphically.

Contributing

Contributions are welcome! If you'd like to improve this project, feel free to fork the repository and submit a pull request.

Contact

For any questions or feedback, feel free to reach out!

• Email: [email protected]
• GitHub: https://github.com/Elango001

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Enjoy predicting stock prices! 🚀