Welcome to my GitHub repository on stock price prediction using LSTM! In this project, I explored the predictive power of the LSTM model to make accurate stock price predictions.
The goal of this project was to train an LSTM (Long Short-Term Memory) model on the stock price data of the company 'Advanced Auto Parts.' The dataset was sourced from Kaggle and served as the basis for our analysis.
I am thrilled to report that my LSTM model achieved an impressive Root Mean Squared Error (RMSE) value of 0.5.
This indicates that, on average, the model's predictions were only off by half a dollar from the actual stock prices. Such accuracy is demonstrates the potential of using LSTM models for this type of task.
When using LSTM models or any predictive model for stock price prediction, keep the following in mind:
Generalization: Ensure that your LSTM model generalizes well on unseen data to avoid overfitting.
Market Dynamics: The stock market is influenced by various factors beyond historical price data. Consider incorporating other indicators or models to complement LSTM predictions.
Risk Management: Predictive models do not eliminate risks associated with stock market investments. Implement proper risk management strategies and exercise caution in real-world trading decisions.
This project was my contribution to the group project titled 'Exploring The Predictive Power Of ARIMA, XGBoost, And LSTM Models On Stock Market Data' for the EAS 508 Statistical Learning and Data Mining course. Read the complete report
I hope this repository serves as a useful resource for anyone interested in stock price prediction using LSTM models. Feel free to explore, contribute, and provide feedback to enhance the project further!
Happy predicting and happy coding! 🚀