This repository contains an ML workflow to predict house prices in Ames, Iowa (data source). The analysis presented in this repository was done as part of the Machine Learning module of the GeoDSc track of the Copernicus Master in Digital Earth.
The notebooks detail data exploration and preparation followed by 3 regression models applied to the data with different configurations. These different models include Basic Linear Regression, Linear Regression with ElasticNet Regularization, Random Forest Model, Random Forest with Boruta Feature Selection, and Catboost Model. The notebooks also demonstrate workflows for hyperparameter tuning for each of the models.