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.
- Python – 3.7.2
- Anaconda Navigator – 1.9.6
- Jupyter Notebook – 5.7.4
- Analyzing: Numpy, Pandas
- Visualization: Matplotlib, Seaborn
- Modeling: Sci-Kit Learn, XGBoost
The competition is hosted on Kaggle. https://www.kaggle.com/c/house-prices-advanced-regression-techniques/overview