In this project, we'll predict future season stats for baseball players. Specifically, we'll predict the wins above replacement (WAR) a player will generate next season. First we download baseball season data using pybaseball and clean it. We'll do feature selection using a sequential feature selector to identify the most promising predictors for machine learning. We'll then train a ridge regression model to predict future season WAR. We'll measure error and improve the model.
We now have a model that can predict future season WAR
Project Steps
- Download baseball season data
- Clean the data and prepare it for ML
- Run feature selection
- Create a machine learning model and estimate accuracy
- Improve accuracy
To follow this project, please install the following locally:
- JupyerLab
- Python 3.8+
- Python packages
- pybaseball
- pandas
- scikit-learn
- numpy
We'll download the data during this project, using the pybaseball package.