north0n-fi / house-prices-advanced-regression-techniques

This is my contribution to a competition on kaggle.com, where you have a dataset with 79 explanatory variables describing (almost) every aspect of c. 1500 residential homes in Ames, Iowa. The aim is to predict the final price of each home.

House-Prices-Advanced-Regression-Techniques

This is my contribution to a competition on kaggle.com, where you have a dataset with 79 explanatory variables describing (almost) every aspect of c. 1500 residential homes in Ames, Iowa. The aim is to predict the final price of each home. I submitted my estimates to the comptetition in early August 2018, resulting in a 2060th place among 4653 competitors, placing my solution in the "top 44%". Submissions are evaluated on Root-Mean-Squared-Error (RMSE) between the logarithm of the predicted value and the logarithm of the observed sales price. (Taking logs means that errors in predicting expensive houses and cheap houses will affect the result equally.) The RMSE of my model was rated at 0.13992. The public Leaderboard is found here https://www.kaggle.com/c/house-prices-advanced-regression-techniques/leaderboard