📈 Stock Price Prediction using LSTM (Soft Computing)

A deep learning project that predicts stock prices using Long Short-Term Memory (LSTM) networks, a soft computing technique under artificial neural networks.

🚀 Features

• Downloads real-time stock data from Yahoo Finance
• Implements 3-layer LSTM model with Dropout regularization
• Includes model checkpointing and early stopping
• Visualizes predictions vs actual prices
• Ready-to-use modular code structure

📂 Project Structure

stock-price-detection/
├── data/                   # Sample stock datasets
├── models/                 # Saved model weights (.h5)
├── data_loader.py          # Data fetching and preprocessing
├── training.py             # Model training script
├── predict.py              # Prediction and visualization
├── requirements.txt        # Dependency list
└── README.md               # This file

⚙️ Installation

1. Clone the repository:

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

2. Install dependencies:

pip install -r requirements.txt

🧠 Training the Model

python training.py

• Default ticker: AAPL (Apple Inc.)
• Time period: 2010-01-01 to 2020-12-31
• Model saved as models/best_model.h5

🔮 Making Predictions

python predict.py

• Tests on 2021 data by default
• Generates stock_prediction.png comparing actual vs predicted prices

📊 Sample Output

🛠️ Customization

• Change stock ticker in both files
• Modify look_back period (default: 60 days)
• Adjust LSTM layers in training.py

🤝 Contributing

1. Fork the project
2. Create your feature branch (git checkout -b feature/AmazingFeature)
3. Commit changes (git commit -m 'Add some feature')
4. Push to branch (git push origin feature/AmazingFeature)
5. Open a Pull Request

📜 License Distributed under MIT License. See LICENSE for more information.

📧 Contact

Om Surve- LinkedIn - omsurve310704@outlook.com

Project Link: https://github.com/omsurve31/stock-price-detection