This project focuses on predicting stock prices using Long Short-Term Memory (LSTM) networks. The implementation is based on historical stock data of Tata Consultancy Services (TCS) and utilizes deep learning techniques to forecast future stock prices.
- Uses Yahoo Finance API to fetch stock prices.
- Preprocesses data with MinMaxScaler.
- Implements an LSTM model using TensorFlow/Keras.
- Evaluates performance using MAE, MSE, and RMSE.
- Visualizes actual vs. predicted prices.
- Python
- TensorFlow / Keras
- Pandas, NumPy
- Matplotlib
- Yahoo Finance API
📂 Stock-Market-Prediction │── 📜 Design_project_report.pdf │── 📜 lstm_stockprediction.ipynb │── 📜 Presentation_Poster.pdf │── 📜 README.md
- Clone the repository:
git clone https://github.com/PRATIKSINDHIYA/Design-project-1.git
- Install dependencies:
pip install -r requirements.txt
- Run the Jupyter Notebook:
jupyter notebook lstm_stockprediction.ipynb
Future Enhancements
Add sentiment analysis for better predictions.
Include macroeconomic indicators.
Experiment with GRUs and Hybrid Models.