Marketing Analytics: Predicting Customer Churn in Python

You move through exploratory analysis, preprocessing, feature engineering and modelling — and the reason to do it on real, messy data rather than a clean toy set is that the hard part of churn work isn't the algorithm, it's preparing the data and framing the question. Predicting who's about to leave is one of the highest-value things analytics does for a subscription business, which makes this a genuinely useful applied project to have built and be able to talk through.

It suits people who already have Python and some ML basics and want a concrete marketing use case, and it's the wrong fit if you lack that grounding or you want theory. It's on DataCamp's subscription (about $14/month billed annually, first chapter free), and finishing earns a completion certificate — a learning record, nothing more formal. The workflow it teaches transfers to any churn problem (as of 2026).

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.