✨ Executive Summary

Customer churn is a critical challenge for subscription‑based businesses, as retaining existing customers is often more cost‑effective than acquiring new ones. This case study applies predictive modeling using Python (pandas, scikit‑learn) on the Telco Customer Churn dataset (~7,043 customers, 20+ features) to identify at‑risk customers and recommend retention strategies.

Key findings (to be finalized after modeling):

• Overall churn rate is (~26.5%).
• Contract type, tenure, and monthly charges are the strongest predictors of churn.
• Customers on month‑to‑month contracts with high monthly charges are most likely to churn.
• Machine learning models (Logistic Regression, Random Forest) achieved strong predictive performance (ROC‑AUC ~0.8).
• Targeted retention strategies could significantly reduce churn and improve customer lifetime value.

📌 Project Overview

• Domain: Customer Analytics / Predictive Modeling
• Dataset: Telco Customer Churn dataset (~7,043 customers, 20+ service and account features, including churn labels)
• Tools Used: Python (pandas, NumPy, scikit‑learn, matplotlib, seaborn), Jupyter/Colab
• Deliverables: Clean dataset & reproducible notebook, churn prediction models (Logistic Regression, Random Forest), evaluation metrics (Accuracy, Precision, Recall, ROC‑AUC), visuals (EDA plots, feature importance, ROC curve), executive summary, and optional slide deck

Scope: Build a predictive model to classify customers as “Churn” or “Not Churn,” identify the strongest predictors of churn, and provide actionable retention strategies for business teams.

❓ Business Question

Primary Question:

How can we accurately predict which customers are most likely to churn and translate those insights into effective retention strategies?