Purpose

This analysis applies a Random Forest ensemble classifier to predict customer churn and identify the key drivers influencing churn decisions. The model uses 300 decision trees across 8 customer features to achieve robust classification performance while simultaneously ranking feature importance to guide business strategy.

Key Findings

OOB Accuracy: 88.8% (11.2% miss rate) — The out-of-bag error estimate indicates strong generalization performance without requiring a separate test set
Top Driver: support_tickets dominates with importance score 57.58 (100% relative importance) — Customer support interactions are the strongest churn predictor
Feature Hierarchy: tenure_months (75.4%) and monthly_charges (60.4%) rank second and third, showing tenure and pricing significantly influence churn
Model Stability: OOB error converges by ~100 trees, suggesting 300 trees provides stable, reliable predictions

Interpretation

The Random Forest model successfully identifies support ticket volume as the dominant churn signal, with longer tenure and higher charges providing secondary predictive power. The 88.8% accuracy demonstrates the model captures meaningful patterns in the 500-customer dataset. The convergence of out-of-bag scores demonstrates the ensemble has learned stable decision boundaries, making predictions reliable for unseen customers.

Context

As a black-box ensemble, the model sacrif