$ pip install -r requirements.txt && streamlit run app.py
Key results
Random Forest with isotonic calibration selected as the best model across three candidates
0.775
AUC-ROC
37.7%
Top decile response rate
63.2%
Top 3 deciles capture
2,155%
Targeted campaign ROI
Methodology
Built on 8,000 synthetic customer records with demographics, usage patterns, service subscriptions, and campaign response history at a 12% baseline response rate. Engineered features include revenue per tenure, usage intensity composite, service count, upsell headroom, and interaction terms. Three classifiers were trained with class balancing, then calibrated via isotonic regression to produce reliable probability estimates. Decile analysis ranks customers for targeted outreach.