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.

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.

To follow this project, please install the following locally:

JupyterLab

Python 3.8+

Python packages:

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.

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

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.

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.

This project is licensed under the MIT License.

This project uses the yfinance package for downloading financial data.

The machine learning aspects of the project are implemented using the scikit-learn library.