An XGBoost model in Python that classifies if a customer will cancel his/her hotel booking or not. I also use counterfactuals guided by prototypes from the Alibi package to explore the minimum changes needed to flip a prediction from canceled to not canceled and vice versa.

The project 'churn_xgboost_alibi_lennart_wallentin.ipynb' is mainly structured as follows:

(1) Data preparation

(1.1) Data presentation
(1.2) Handling NA and missing values
(1.3) Check data types and outliers 
(1.4) Correlation - Pearson and Cramer’s V 
(1.5) Avoid unique values
(1.6) Encoding and create train and test sets		

(2) XGBoost

(2.1) Hyperparamter tuning - Bayesian optimization
(2.2) Evaluate the model 

(3) Alibi

(3.1) Counterfactuals Guided by Prototypes - in action

The xlsx-file, ‘bayesian_optimization_iterations.xlsx’ contains all the Bayesian optimization iterations.

The dataset that is used for this project is in the csv-file 'data_churn_xgboost_alibi_lennart_wallentin.csv'