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📘 README.md — Stock Price Prediction using LSTM

# 📈 Stock Price Prediction using LSTM

This project is a machine learning-based application that predicts future stock prices using a Long Short-Term Memory (LSTM) neural network. It also features an interactive Streamlit web app for visualizing actual vs predicted stock prices.

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## 🚀 Features

- 💹 Predict stock prices based on historical data
- 📊 Visualize real vs predicted prices
- 🧠 Trained using LSTM (Recurrent Neural Network)
- 🛠️ Built with Python, Keras, Pandas, NumPy, Streamlit
- 📁 Jupyter Notebook for model training
- 🌐 Streamlit app for UI

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## 📂 Project Structure

Stock-Price-Prediction/ │ ├── app.py # Streamlit web app ├── stock_predictor.ipynb # Jupyter Notebook (LSTM model training) ├── real_prices.npy # Saved real prices (for plotting) ├── predicted_prices.npy # Saved model predictions ├── venv/ # Python virtual environment └── README.md # This file

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## 🧠 LSTM Model Overview

- Data Source: Yahoo Finance (`yfinance` library)
- Model: Sequential LSTM model using TensorFlow/Keras
- Input: 60-day window of past stock prices
- Output: Next day's predicted price
- Loss Function: Mean Squared Error (MSE)
- Optimizer: Adam

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## ⚙️ Installation & Setup

1. **Clone the repo**
```bash
git clone https://github.com/yourusername/stock-price-prediction.git
cd stock-price-prediction

2. Create virtual environment (Python 3.10 recommended)

python -m venv venv
venv\Scripts\activate  # On Windows
# source venv/bin/activate  # On macOS/Linux

3. Install dependencies

pip install -r requirements.txt

4. Run Streamlit app

python -m streamlit run app.py

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🧪 How to Train the Model

Open the stock_predictor.ipynb notebook in VS Code or JupyterLab. Run the cells to:

• Load stock data
• Preprocess and scale it
• Build and train the LSTM model
• Generate and save real_prices.npy and predicted_prices.npy

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🖼️ Example Output

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📦 Dependencies

• Python 3.10
• pandas
• numpy
• yfinance
• matplotlib
• scikit-learn
• tensorflow / keras
• streamlit

Install everything with:

pip install -r requirements.txt

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✨ Future Improvements

• Add dynamic model training based on user-selected stocks
• Integrate more advanced models (e.g., ARIMA, Prophet)
• Deploy app online via Streamlit Cloud or HuggingFace Spaces

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🙌 Acknowledgements

• Data from Yahoo Finance
• Model inspiration from TensorFlow/Keras examples

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📜 License

This project is licensed under the MIT License.

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