House Prices - Advanced Regression Techniques

Overview

This competition challenges you to predict house sale prices in Ames, Iowa, using a dataset with 79 features describing various aspects of residential homes. It's a great opportunity to practice creative feature engineering and advanced regression techniques like random forests and gradient boosting.

Requirements

You can compete individually or form a team. This competition is suitable for those with some experience in R or Python and basic machine learning concepts. Your task is to predict the 'SalePrice' for each house in the test set. Submissions are evaluated using the Root Mean Squared Logarithmic Error (RMSLE) between the predicted and actual sale prices.

Prizes

This competition is considered a 'Getting Started' event, meaning there are no cash prizes or medals. The main reward is the knowledge and practical experience you'll gain in advanced regression techniques and feature engineering.