Telco Churn Analysis
Developed a churn prediction model using AllKNN with hyperparameter tuning, focused on minimizing false negatives. Achieved 93.7% recall, reducing potential high-risk churn losses by $18.8K and cutting total misclassification costs by $48.5K, outperforming benchmark models like XGBoost and Random Forest.
Proof points
- 93.7% Recall
- -$48.5K Costs
Technologies
Python, Scikit-learn, XGBoost, Pandas