We recently finished a project for the DSPT2 section of Lambda School's part time data science course. For my project, I decided to use a Kaggle data set of video game sales. The goal of the project is to predict global sales based on critic score, critic count, publisher, platform, etc.
For this project, I decided to lean heavily on Randomized Search Cross Validation and xgboost's XGBRegressor (using the mae eval_metric). However, I first started out with a baseline of the average global sales for the entire data set. This led me to a baseline mae of 0.6605.
Then after applying a randomized search cross validation to xgboost's XGBRegressor, I ended up with an mae of 0.4875. This beats the baseline by 0.173.
I also plotted the permutation importances of all the used features, which can be viewed below:
That's it. Thanks for reading!
Top comments (0)