📈 Stock Market Predictor using LSTM

A deep learning-based model that predicts stock prices using LSTM networks and historical data from Yahoo Finance. The project includes a Streamlit web app for interactive visualization and forecasting.

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🔧 Features

• Retrieves historical stock data using yfinance
• Applies 50, 100, and 200-day moving averages
• Trains a multi-layer LSTM neural network
• Predicts future stock prices
• Deploys an interactive Streamlit dashboard

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🚀 How to Use

1. Clone the Repository

git clone https://github.com/your-username/stock-market-predictor.git cd stock-market-predictor

2. Create a Virtual Environment (Optional but Recommended)

python -m venv venv

3. Activate the environment

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

4. Install Dependencies

pip install -r requirements.txt

5. Run the Streamlit App

streamlit run app.py

6. Interact with the Dashboard

• Enter a stock ticker (e.g., GOOG)
• View historical stock data with moving averages
• Compare original vs predicted prices

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🧠 Tech Stack

• Python
• TensorFlow/Keras
• LSTM
• Scikit-learn
• yfinance
• Streamlit
• Matplotlib

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📌 Notes

• Run model_training.py to retrain the model with new data.
• The model uses the last 100 days of closing prices for predictions.

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📬 Contact

For any feedback or issues, please open an issue or reach out via GitHub.