| --- |
| datasets: |
| - scikit-learn/churn-prediction |
| - aai510-group1/telco-customer-churn |
| language: |
| - en |
| - ar |
| --- |
| # Customer Churn Prediction |
| Predicting telecom customer churn using Random Forest & SMOTE to enable proactive retention strategies. |
|
|
| ## Problem |
| Predict which telecom customers are likely to churn |
| to enable proactive retention strategies. |
|
|
| ## Results |
| | Model | Accuracy | ROC-AUC | Recall | |
| |-------|----------|---------|--------| |
| | Random Forest | 79% | 0.813 | 49% | |
| | Balanced RF | 78% | 0.816 | 45% | |
| | SMOTE + RF | 77% | 0.809 | 56% ✅ | |
|
|
| ## Key Insights |
| - High monthly charges is the top churn driver |
| - Month-to-month contracts have highest churn risk |
| - New customers (< 6 months) are most vulnerable |
|
|
| ## Business Impact |
| SMOTE model saves ~$14,000 more annually |
| compared to baseline by identifying 28 additional |
| at-risk customers. |
|
|
| ## Tools |
| Python | Scikit-learn | SMOTE | Pandas | Matplotlib |
