This repository contains a Python Jupyter notebook explaining how to implement some regression models. We apply the models to the Ames Housing dataset, a classical set for advanced regression tasks.
We try the following models:
- Multiple Linear Regression with PCA and target quantile transformation
- LASSO Regression with target quantile transformation
- AdaBoost
- XGBoost
- CatBoost
- A Stacked Regressor combining all the models above, with a CatBoost final estimator
We also build complete pipeline that include automatic data imputation, scaling, and interfacing with Pandas dataframes.
This code is distributed under the GNU General Public License v3.0.