One of the data science skills I want to play around with is deriving insights from data that publically available. Here, lets use some data on SF employee compensation and see what we can learn from the data.

First, per usual, load the dependencies.

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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My goal is to learn how to scrape data using python and do some quick data analysis.

This is my first time scraping from the web. I found this documentation extremely helpful. Here, I'm pulling in the shot log for Andrew Wiggins, the NBA Rookie of the Year for the 2014-2015 season.

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