This is my code for an in-class competition at UT Austin. The goal was to take part in the following Kaggle competition and compete on the public leaderboard to get the best possible score.
House Prices - Advanced Regression Techniques
My Work:
- Implemented machine learning models (XGB, LGBM, NuSVR etc) using Scikit‐Learn to train a dataset with 288 features
- Applied feature engineering through label encoding, outlier detection, feature skewness and variance analysis to improve model’s fit
- Used ensembling and stacking to generate predictions that achieved a test RMSE score among top 2% on Kaggle’s public leaderboard