Goal is to predict the sales price for each house. For each Id in the test set, predicting the value of the SalePrice variable.
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
- Jupyter Notebook
- NumPy, Pandas (For data manipulation)
- Sklearn
- Matplotlib (To visualize the data using graphs)