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Name: Lennart Wallentin
Type: User
Location: Stockholm, Sweden
Name: Lennart Wallentin
Type: User
Location: Stockholm, Sweden
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 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.
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.
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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