This competition challenges participants to predict the final sales price of residential homes in Ames, Iowa. With 79 explanatory variables describing almost every aspect of these homes, the dataset proves that factors beyond simple features like the number of bedrooms or a white-picket fence significantly influence price negotiations.
Who Can Participate This is a "Getting Started" competition designed for individuals with some experience in R or Python and machine learning basics. It is an ideal opportunity for data science students who have completed an online course in machine learning and wish to expand their skill set before attempting featured competitions. These competitions are non-competitive, feature no cash prizes, and operate on a rolling timeline.
Practice Skills Participants will focus on: * Creative feature engineering * Advanced regression techniques, including random forests and gradient boosting
Evaluation
The goal is to predict the SalePrice variable for each house ID in the test set. Submissions are evaluated based on the Root-Mean-Squared-Error (RMSE) between the logarithm of the predicted value and the logarithm of the observed sales price. This approach ensures that errors in predicting expensive houses and cheap houses affect the result equally.
Submission Format
Submissions must be a CSV file containing a header and two columns: Id and SalePrice.