Program that predicts the NBA mvp based on data from previous years.

NBA MVP Predictor

A machine learning model using RandomForest Regression that predicts NBA MVP's using player data.
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About The Project

This project utilizes RandomForest Regression ML model to predict the NBA MVP. Now you may think that this is not a regression problem, but more of a classification problem, however our approach to predicting MVP consists of predicting a numerical variable called MVP win share. From that prediction, the player in the season with the highest MVP win share is predicted to be the MVP. As you can see structuring the problem like this lends more towards a regression solution.

Our machine learning model is trained on data from 1980-2010, and then we use that to predict the MVP's for the 2011-2021 season.

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Built With

• Python
• Pandas
• scikit-learn
• seaborn
• Matplotlib

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Examples of Graphs Used

Usage

To run this model on your system, download the jupyter notebook, and data. Then within the file change the URL for the raw_mvp_data variable to the path where the data is located on your system.

Results

The model achieved an R^2 value of 0.6127, guessing 8/10 of it's predictions correctly.

Acknowledgements

Inspiration from this article: https://towardsdatascience.com/predicting-the-next-nba-mvp-using-machine-learning-62615bfcff75