Project Overview

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. We'll first 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.

At the end, you'll have a model that can predict future season WAR, along with next steps to improve the model.

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

Code

You can find the code for this project here

File overview:

• next_war.ipynb - a Jupyter notebook that contains the code to predict next season WAR.

Local Setup

Installation

To follow this project, please install the following locally:

• JupyerLab
• Python 3.8+
• Python packages
  • pybaseball
  • pandas
  • scikit-learn
  • numpy

Data

We'll download the data during this project, using the pybaseball package.