StockistAI is a hybrid stock price prediction application that leverages LSTM (Deep Learning) and SVM (Machine Learning) to analyze and forecast stock trends. Built with Streamlit, TensorFlow, Scikit-Learn, and Yahoo Finance API, this platform enables users to visualize stock data and make informed financial decisions.
✅ Real-Time Stock Data – Fetch stock prices directly from Yahoo Finance
✅ LSTM Model for Deep Learning Forecasting – Captures sequential patterns in stock trends
✅ SVM Model for Machine Learning Forecasting – Enhances predictions with statistical learning
✅ Hybrid Model (LSTM + SVM) – Combines both models for optimized performance
✅ Customizable Parameters – Adjust LSTM time steps, epochs, batch size, and SVM kernel
✅ Interactive UI with Streamlit – Simple, intuitive interface for easy stock analysis
✅ Performance Metrics – Evaluate predictions using MAE and MSE
✅ Download Predictions as CSV – Export results for further analysis
git clone https://github.com/your-username/StockistAI.git
cd StockistAIpip install -r requirements.txtstreamlit run stockistai.py- Enter the stock ticker (e.g., AAPL, TSLA) in the sidebar.
- Adjust the LSTM time step, epochs, batch size, and SVM kernel type.
- Click on Run Prediction to start forecasting.
- View actual vs predicted stock prices with interactive charts.
- Download prediction results as a CSV file for analysis.
- LSTM (Long Short-Term Memory): A deep learning model designed for time series forecasting.
- SVM (Support Vector Machine): A machine learning model that identifies stock price trends.
- Hybrid Approach: Combines LSTM and SVM predictions for enhanced accuracy.
Check out the live demo: Coming Soon!
Want to improve StockistAI? Feel free to fork this repo and submit a pull request!
Ashik Sharon M