Customer Churn Classification - Develop a model to classify customers who churn based on their usage and demographics.

Description:
The Telco customer churn data contains information about a fictional telco company that provided home phone and Internet services to 7043 customers in California in Q3. It indicates which customers have left, stayed, or signed up for their service. Multiple important demographics are included for each customer, as well as a Satisfaction Score, Churn Score, and Customer Lifetime Value (CLTV) index.

Data:
https://www.kaggle.com/datasets/blastchar/telco-customer-churn/data

Objective:
Build a classification model to predict customer churn. Handle class imbalance, perform feature engineering, and evaluate model performance using metrics such as precision, recall, F1-score, and ROC-AUC. Additionally, identify customer segments at highest risk, determine key churn drivers, and provide actionable recommendations for reducing churn.

References:
https://community.ibm.com/community/user/blogs/steven-macko/2019/07/11/telco-customer-churn-1113 https://scikit-learn.org/stable/user_guide.html