Marketing Analytics: Predicting Customer Churn in Python

Marketing Analytics: Predicting Customer Churn in Python walks through the full churn-modeling workflow on the real Telco Churn Dataset: exploratory analysis and visualization, preprocessing (encoding, scaling, feature engineering), building scikit-learn classification models, evaluating them with accuracy, precision, recall, ROC-AUC and F1, and finally tuning hyperparameters to understand what actually drives churn.

The honest take: this is squarely about telecom/subscription-style churn — the dataset and examples are industry-specific, but the underlying classification workflow transfers directly to SaaS, cable, or any subscription business. No machine learning background is required, but comfort with pandas is assumed.