Ask a home buyer to describe their dream house, and they probably won't begin with the height of the basement ceiling or the proximity to an east-west railroad. But this playground competition's dataset proves that much more influences price negotiations than the number of bedrooms or a white-picket fence.
With 79 explanatory variables describing (almost) every aspect of residential homes in Ames, Iowa, this competition challenges you to predict the final price of each home.
In a form of a jupyter notebook, my solution goes through the basic steps of a data science pipeline:
- Exploratory data analysis with visualizations
- Data cleaning
- Feature engineering
- Modeling