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Lennart Wallentin's Projects

churn-hotel-xgboost-alibi icon churn-hotel-xgboost-alibi

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.

feature_selection_functions icon feature_selection_functions

Feature selection is widely used in nearly all data science pipelines. Hence I have created functions that do a form of backward stepwise selection based on the XGBoost classifier feature importance and a set of other input values with the goal to return the number of features to keep in regard to a prefered AUC-score.

neural_network_stacked_generalization_xgboost_glm icon neural_network_stacked_generalization_xgboost_glm

I showcase that I have broad set of skills regarding machine learning algorithms since I use Logistic Regression, XGBoost and Neural Networks in this project. Especially that I have a good understanding regarding neural networks and the Keras library.

passenger_satisfaction_stacking_anova icon passenger_satisfaction_stacking_anova

The main objectives of this project are Stacked Generalization (Stacking) and comparing models with ANOVA tests. To do that I classify if a person flying with an unnamed north american airline company is satisfied or neutral/dissatisfied with their flight.

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