In this competition, participants will develop regression models to predict median house values using a highly engineered and preprocessed version of the California Housing dataset. This isn't the standard dataset you're used to — it’s been extensively transformed through rigorous feature engineering. The result is what may be one of the most feature-dense housing datasets ever curated.

The goal is to accurately model the relationship between geographic, economic, and engineered signals and the median house value in California. This serves as both a machine learning challenge and an opportunity to explore the limits of tabular model performance under heavy preprocessing.

Participants are encouraged to explore ensemble models (e.g., XGBoost, CatBoost, LightGBM), neural networks, or even hybrid modeling strategies. Strong performance will hinge on navigating the high-dimensional feature space effectively, leveraging regularization, and understanding the engineered features.