The Housing Prices Competition for Kaggle Learn Users is designed for participants who have some experience with R or Python and machine learning basics. It serves as a perfect opportunity for data science students who have completed an online course in machine learning to expand their skill set before attempting featured competitions.
This competition challenges you to predict the final price of residential homes using a dataset containing 79 explanatory variables describing almost every aspect of houses in Ames, Iowa. The goal is to apply creative feature engineering and advanced regression techniques, such as random forest and gradient boosting, to determine the sales price for each house.
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 metric ensures that errors in predicting expensive houses and cheap houses affect the result equally. Participants must submit a file containing the Id and the predicted SalePrice for each entry in the test set.
As a "Getting Started" competition created by Kaggle data scientists, it offers a non-competitive environment to get familiar with Kaggle's platform, learn basic machine learning concepts, and connect with the community. These competitions operate on a rolling timeline and do not offer cash prizes.