⚠ The Challenge
A telecom company was losing customers without understanding why. Goal was identifying customers likely to leave before they did — allowing retention team to intervene with targeted offers.
💡 The Approach
Complete ML pipeline training 4 models simultaneously. Rather than picking one model upfront, trained all 4 and let metrics decide which performs best on the specific dataset.
🔄 Step-by-Step Process
Loaded 7,043 telecom customer records with 20+ features including usage and contract type
Cleaned data, handled missing TotalCharges values, encoded categorical variables
Applied StandardScaler normalization for scale-sensitive models
Trained 4 models: Logistic Regression, Random Forest, XGBoost, Gradient Boosting
Evaluated all models with AUC-ROC, precision, recall, F1-score comparison charts
Built feature importance visualization revealing top churn drivers for business insights
✓ Final Result
Gradient Boosting achieved best AUC of 0.8438. Feature importance revealed contract type, tenure, and monthly charges as strongest churn predictors. Real-time single-customer churn probability scoring.
📚 Key Lesson
Always train multiple models and compare. Random Forest may outperform XGBoost on some datasets. The extra 10 minutes of training all models is always worth the insight.