Customer Churn Prediction for an Indian Telco
Real-world project · AICTE-aligned · AI-graded · Audit-ready certificate
About this project
Use the public Telco Churn dataset (or simulated Jio/Airtel-style data). Build classifiers (logistic regression, random forest, XGBoost). Tune via cross-validation. Interpret model with SHAP. Recommend 3 retention actions based on highest-impact features.
Course Learning Outcomes (CLOs):
CLO1: Apply tree-based + linear classifiers to a tabular business problem.
CLO2: Handle class imbalance using appropriate techniques.
CLO3: Tune hyperparameters with cross-validation.
CLO4: Explain model predictions with SHAP.
CLO5: Translate model insights into business actions.
Industry/societal relevance: Jio, Airtel, Vi all run churn-prediction pipelines internally; foundational analytics-team project. Also relevant to Indian fintech (PhonePe, GPay).
Milestones
Skills you'll learn
Tools used
Prerequisites
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Be the first to mentorYou'll earn — Certificate (PDF)
AICTE-aligned Project Completion Certificate
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AICTE-aligned
Certificate of Project Completion
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has successfully completed the project
Customer Churn Prediction for an Indian Telco
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