🚀 This project is a Flask-based stock prediction web application that uses machine learning to predict whether a stock's price will rise or fall in the next 5 days based on technical indicators.

✅ Enter a stock ticker (e.g., AAPL, TSLA, NVDA).
✅ Fetches real-time stock data from Yahoo Finance.
✅ Computes technical indicators (SMA, EMA, RSI, MACD, OBV, etc.).
✅ Uses a pre-trained machine learning model to make predictions.
✅ User-friendly web interface built with HTML & Flask.
✅ Deployable on Render or Heroku for live usage.

git clone https://github.com/Swaraj-sync/StockMark.git
cd StockMark

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

pip install -r requirements.txt

python app.py

Open http://127.0.0.1:5000/ in your browser.

1. Push your code to GitHub/GitLab.
2. Go to Render and create a new Web Service.
3. Connect it to your GitHub repository.
4. Set the Start Command to:

   gunicorn app:app
   

5. Deploy & test your app!

📁 Flask-Stock-Prediction/
│-- 📁 templates/          # HTML templates for UI
│   │-- index.html
│   │-- layout.html
│
│-- 📁 static/             # CSS & JavaScript (if needed)
│-- app.py                 # Flask web application
│-- model.pkl              # Pre-trained ML model
│-- scaler.pkl             # Pre-trained scaler
│-- selector.pkl           # Feature selector
│-- requirements.txt       # Python dependencies
│-- Procfile               # For deployment on Heroku
│-- README.md              # Project documentation

• Flask – Backend Framework
• yFinance – Fetches Stock Data
• scikit-learn – Machine Learning
• Gunicorn – Deployment Server
• Render/Heroku – Hosting

💡 Swaraj Patil And Amanraj Mishra

Feel free to fork, contribute, or suggest improvements! 🚀

📧 Contact: patilswaraj1111@gmail.com