Stock Market Price Prediction System Using Machine Learning

This project leverages Machine Learning, specifically LSTM (Long Short-Term Memory) networks, to predict short-term stock price movements. By integrating real-time data, it provides both actual stock prices and predicted stock trends for the next 30 days, enabling investors to make more informed decisions.

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Table of Contents

1. Project Overview
2. Features
3. Technology Stack
4. Installation
5. Usage
6. Contributing
7. License

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Project Overview

The Stock Market Prediction System uses LSTM neural networks to predict stock prices for the next 30 days based on historical price data. The system fetches real-time data from Yahoo Finance API and dynamically updates predictions. A user-friendly interface is provided to visualize actual vs. predicted stock prices using Chart.js.

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Features

• Real-Time Stock Data: Fetches historical and real-time stock prices.
• Prediction for the Next 30 Days: Predicts short-term trends using LSTM models.
• Data Visualization: Displays actual and predicted prices using interactive Chart.js visualizations.
• Model Retraining: Dynamic retraining to improve prediction accuracy with new data.
• User-Friendly Interface: An intuitive dashboard for both novice and experienced investors.

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Technology Stack

• Frontend:

  • HTML, CSS, JavaScript (for user interface and real-time visualizations)
  • Chart.js (for data visualizations)

• Backend:

  • Flask (for the backend server and API handling)
  • Python (for data processing and machine learning)
  • yFinance (for fetching stock data)
  • TensorFlow / Keras (for training LSTM models)

• Database:

  • No database required for this project. All data is fetched dynamically via the Yahoo Finance API.

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Installation

Follow these steps to run the project locally.

1. Clone the repository:

   git clone https://github.com/iamDeepakHR/Stock-Price-Prediction-Using-Machine-Learning.git
   cd Stock-Price-Prediction-Using-Machine-Learning

2. Create a virtual environment (recommended):

   python -m venv venv
   source venv/bin/activate  # On Windows, use `venv\Scripts\activate`

3. Install dependencies:

   pip install -r requirements.txt

4. Download the necessary model and scaler files:

   • Place the trained LSTM model (stock_lstm_model.keras) and scaler (scaler.pkl) into the model/ directory.

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Usage

1. Run the Flask app:

   python app.py

2. Access the app: Open your browser and navigate to http://127.0.0.1:5000/.

3. Predict Stock Prices:

   • Enter a stock symbol (e.g., AAPL, GOOG, etc.) in the input field and click "Predict."
   • View the actual stock data and predicted stock prices for the next 30 days on the interactive graph.

4. Train the Model:

   • If you want to retrain the model, click the "Train Model" button to trigger training on new data.
   • The model will be retrained with the latest stock data and saved for future use.

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Contributing

We welcome contributions to enhance the project further. If you would like to contribute, please follow these steps:

1. Fork the repository.
2. Create a new branch (git checkout -b feature-name).
3. Make your changes and commit them (git commit -m 'Add feature').
4. Push to your forked repository (git push origin feature-name).
5. Submit a pull request for review.

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License

This project is licensed under the MIT License - see the LICENSE file for details.

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Enjoy using the Stock Market Prediction System and happy investing!