• Performed Synthetic Minority Oversampling Technique (SMOTE) to overcome the problem of imbalance class in the customer dataset by synthesizing new examples from the existing examples.
• Built a XGBoost model and achieved over 93% AUC score in predicting churn of the customer.
• Found out the most important feature that impacted customer churn was the total transaction count for the past 12 months.

Link to Google Colaboratory Notebook with Explanation

• Machine Learning
• Classification