Problem Statement

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.

Results

The metric used to evaluate the model is Root Mean Squared Error (RMSE) with lower the RMSE, better the model. The model (xgboost) built for the project showed a rmse value of ~ 0.14 on train data and ~ 0.1517 on test data. The model's perfomance on the competition dataset is 0.15153 (Leaderboard rank : 2092/3916)