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.
- Data cleared from empty values;
- Engineered features;
- Dealing with outliers;
- Normalized continuous features;
- Stacked with StackingCVRegressor and blended regressors for predicting houses prices.
- Python Version: 3.7.4;
- Packages: pandas, numpy, sklearn, matplotlib, seaborn, datetime, scipy, xgboost, lightgbm, mlxtend.