ML Student Shares Key Insights from Deep-Dive EDA on Ames Housing Dataset

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A machine learning student conducted an in-depth Exploratory Data Analysis (EDA) on the Ames, Iowa Housing Dataset, which covers 2,930 residential properties across 80+ variables. Rather than relying on standard descriptive statistics, the analysis focused on feature engineering, including creating composite metrics like total usable square footage and a price-per-square-foot normalizer. The study found that homes lacking basements or garages carried a median price penalty of 65% to 85%, reflecting the practical demands of Iowa's climate. Counter-intuitively, features like fence presence and alley access correlated with lower sale prices, as these traits are more common in older, denser urban neighborhoods rather than high-value new developments. The full code and findings have been published on GitHub for the broader developer community to review.

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