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roc-auc-analysis

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mudassir-08 / telco-customer-churn-classifier

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This project builds an end-to-end machine learning pipeline to predict customer churn using the Telco dataset. It applies real-world data preprocessing, feature engineering, and multiple ML models with recall optimization for business impact. The final system is production-ready with model serialization using Joblib for deployment.

logistic-regression confusion-matrix data-preprocessing feature-engineering feature-scaling random-forest-classifier missing-value-handling customer-churn-prediction train-test-split xgboost-classifier categorical-encoding column-transformer model-evaluation-metrics class-imbalance-handling precision-recall-f1-score end-to-end-ml-pipeline scikit-learn-pipeline telco-churn-dataset roc-auc-analysis joblib-model-serialization
  • Updated May 9, 2026
  • Jupyter Notebook

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