AAPL Stock Prediction Project Using Data From Jan 1, 2020 to April 20, 2024
This project develops a predictive model using a Long Short-Term Memory (LSTM) network to forecast future stock prices of Apple Inc. (AAPL). It includes a Jupyter notebook that details the entire process from data acquisition to training and predictions. The aim is to utilize deep learning techniques to understand trends and potentially predict future stock movements.
- Data Acquisition: Historical stock price data for AAPL is used as the input. The data covers daily closing prices and is sourced from Yahoo Finance.
- Data Processing: The raw data is transformed into a format suitable for training the LSTM model, involving normalization and windowing for time-series prediction.
- Model Building: The LSTM model consists of several layers designed to capture the temporal dependencies in the stock price data.
- Training and Evaluation: The model is trained with historical data, and its performance is evaluated using a test set. The evaluation focuses on the model’s accuracy and its ability to forecast future prices.
- Prediction: The final model is used to predict the stock prices for the upcoming days. The predictions are then visualized against the actual prices to illustrate the model’s effectiveness.
The notebook provides a detailed visual comparison of the predicted prices against the actual stock prices. These results help illustrate the model's predictive power and its limitations. The visualizations highlight areas where the model performs well and where it may require further tuning or additional data.
- Python: The primary programming language used for all aspects of the project.
- TensorFlow and Keras: These libraries are used for building and training the LSTM model.
- Pandas: Utilized for data manipulation and analysis.
- Matplotlib: Used for creating visualizations of the stock data and the model's predictions.
We welcome contributions from the community. Whether it's improving the model, enhancing data preprocessing, or refining the documentation, your contributions are valuable.
This project is released under the MIT License, which allows modification and distribution for both private and commercial use.