Predict Baseball Stats using Machine Learning and Python

We'll predict future season stats for baseball players using machine learning. The stat we'll predict is the wins above replacement (WAR) a player will generate next season.

We'll first download and clean baseball season data using python and pybaseball. 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.

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

You can find the full code here - [project-walkthroughs/baseball_games at master · dataquestio/project-walkthroughs · GitHub](https://github.com/dataquestio/projec...)

Chapters

00:00 Introduction
02:00 - Download the data
05:52 - Creating an ML target
09:15 - Cleaning the data
16:19 - Selecting useful features
27:13 - Making predictions with ML
38:15 - Improving accuracy
49:26 - Diagnosing issues with the model
52:28 - Wrap-up and next steps with the model

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