In the last post, I pulled some NBA shot data for Andrew Wiggins and put that into a dataframe. Here, we will apply some supervised learning techniques from sklearn to build predictive models and then use visualizations to better understand the data.

Some topics we'll explore are prediction error, regularization, and the tradeoff between prediction accuracy and model interpretability.

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