S&P500 Stock Market Index Prediction

This project aims to predict the price of the S&P500 stock market index using machine learning techniques. The project involves data download, model creation, accuracy estimation, backtesting, and model improvement.

Project Overview

Downloading data using the yfinance package.

Creating an initial machine learning model and estimating accuracy.

Building a backtesting engine to more accurately measure accuracy.

Improving the accuracy of the model.

Local Setup

Installation

To follow this project, please install the following locally:

JupyterLab

Python 3.8+

Python packages:

Data

The project downloads all the necessary data using the yfinance package. This package allows for the retrieval of historical stock market data, including the S&P500 index.

File Overview

notebook.ipynb: Jupyter notebook that contains all the code for this project.

Usage

Install the required dependencies mentioned in the Local Setup section.

Open the market_prediction.ipynb notebook in JupyterLab.

Follow the step-by-step instructions in the notebook to run the project code.

Contributing

Contributions to this project are welcome! If you find any issues or have ideas for improvements, please open an issue or submit a pull request.

License

This project is licensed under the

MIT License

Acknowledgements

This project uses the

yfinance

The machine learning aspects of the project are implemented using the

scikit-learn