02_WebScrape-LinearRegression: Scraping MobyGames.com and predicting critic scores

I worked on this project for two weeks as part of the Metis Chicago 2018 Winter Cohort. This was Project 2.

We were given free reign to pick our problem and data sources, with two requirements:

1. Procure our data via web scraping, and
2. Use linear regression to predict a target variable.

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This project is broken into 3 Jupyter Notebooks.

• Notebook 1 - Covers the web-scraping step. Results in:

  1. allgames.pk1: DataFrame with scraped data for all current console games.
  2. score_df.pk1: DataFrame pared down to only the rows that contain the target: critic score.

• Notebook 2 - Data cleaning and transformation. Results in:

  1. df_mod.pk1: The cleaned DataFrame for use in the EDA and modeling steps.

• Notebook 3 - Covers the EDA and linear regression.