Hey everyone! As I’m working through my ML fundamentals, I wanted to share a deep-dive Exploratory Data Analysis (EDA) I just completed on the classic Ames, Iowa Housing Dataset.
If you are unfamiliar, the Ames dataset is basically the "final boss" version of the Boston Housing dataset. It has 2,930 residential properties and 80+ explanatory variables.
Instead of just running a standard df.describe(), I wanted to focus on Feature Engineering and understanding the actual business logic behind the data. Here are my biggest takeaways and a few counter-intuitive findings!
When I first looked at the data, the square footage was split across multiple columns (basement, 1st floor, 2nd floor). Instead of feeding the model raw variables, I created a few composite features:
Total_Usable_SF: I combined Above-Grade Living Area and Total Basement square footage. This gave a much more realistic picture of the home's total usable space.
