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. Ames Houses Prices 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 project aimed on prediction the final price of each home.
Current solution consists of three parts:
- Data exploration, analysis and preprocessing.
- Data types
- Null values
- Correlation
- Feature engineering
- Skewness
- ML model building, evaluation.
- LinearRegression
- Lasso Regression
- Random Forest Regression
- Hyperparameter tuning
- Models comparison
- Prediction conduction.
The final model's MAE is about 10.0% of the mean house price.
The result is presented in a 'eda+prediction' jupyter notebook file.