LSTM for Stock Market Prediction

Project Description

This project aims to develop a Long Short-Term Memory (LSTM) model to predict future stock prices based on historical data. The model is trained and evaluated using real-world stock market data. The project includes data preprocessing, model training, evaluation, and visualization of results.

Table of Contents

• Project Description
• Table of Contents
• How to Run the Project
• Badges
• Conclusion

How to Run the Project

To run the project, follow these steps:

1. Clone the repository.
2. Install the required dependencies.
3. Run the main.py script.

Badges

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Conclusion

This project provided valuable experience in:

• Data preprocessing and normalization techniques.
• Implementing an LSTM model for time series prediction.
• Evaluating model performance using metrics such as Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE).
• Visualizing model predictions and comparing them to actual stock prices.