Achieving Top 0.25% in Kaggle House Price Prediction
Ranked 14 out of 5,881 participants through extensive feature engineering and stacked ensemble modeling.
Introduction
Predicting house prices is a classic machine learning task that requires careful feature engineering and model design. I participated in the Kaggle "House Prices: Advanced Regression Techniques" competition and ranked in the top 0.25% (14/5881) out of around 6,000 participants.
Project Highlights
Extensive Feature Engineering
Created 50+ new features, including polynomial features and interaction terms, to capture complex relationships in the housing data.
Model Development
Implemented and fine-tuned Linear Regression, Random Forest, XGBoost, and Neural Networks for comprehensive model comparison.
Stacked Ensemble
Built an ensemble model combining the best-performing individual models to maximize prediction accuracy.
Impact
This project highlights the ability to design end-to-end ML pipelines, from feature engineering to ensemble modeling, and achieve competitive results in a large-scale machine learning competition.