Predict 30-Day Churn for a Direct-to-Consumer Cosmetics Brand
Overview
What this challenge is about.
Build a churn prediction model for a cosmetics brand using Python and scikit-learn, then earn a verifiable certificate.
The scenario
The client is a privately held direct-to-consumer cosmetics and skincare startup based in Bangalore, India, operating a monthly subscription model with roughly 50,000 active subscribers and a small in-house growth team. After two years of aggressive customer acquisition, rising churn is now eroding the unit economics that its next funding round depends on.
The Brief
What you'll do, and what you'll demonstrate.
Identify which subscribers are most likely to cancel within the next 30 days so the marketing team can intervene proactively with retention offers.
Earning criteria — what you'll demonstrate
- Engineer behavioral features from raw subscriber activity data that meaningfully predict churn without leaking the outcome
- Train and tune interpretable classifiers in scikit-learn while correctly handling class imbalance
- Evaluate a classifier with AUC-ROC and threshold-aware metrics on a held-out split, and justify the choices
- Translate model feature importance into plain-language drivers and concrete retention actions for a non-technical audience
Program Fit
Where this fits in your program.
Sharpens the same skills your degree expects you to demonstrate.
Aligned coursework coming soon.
Skills
Skills you'll demonstrate.
Each one shows up on your verified credential.
- Python
Write clean, efficient Python for data processing, automation, and backend services.
- Scikit Learn
Apply scikit learn to solve real industry problems and demonstrate production-level capability.
- Logistic Regression
Apply logistic regression to solve real industry problems and demonstrate production-level capability.
- Random Forest
Apply random forest to solve real industry problems and demonstrate production-level capability.
- Feature Engineering
Apply feature engineering to solve real industry problems and demonstrate production-level capability.
- Classification
Apply classification to solve real industry problems and demonstrate production-level capability.
Careers
Career paths this challenge builds toward
Completing this challenge demonstrates skills that transfer directly to these roles:
Data Scientist
This challenge mirrors a core data science loop: framing a business problem, engineering features from behavioral data, and shipping an evaluated, interpretable model. It builds the judgment to balance accuracy with explainability that hiring teams probe for.
This challenge sharpens
- feature-engineering
- classification
- scikit-learn
Machine Learning Engineer
Building a reproducible scikit-learn pipeline that handles imbalance and runs end to end develops the engineering discipline ML roles demand, bridging from exploratory modeling toward production-ready, maintainable prediction code.
This challenge sharpens
- python
- scikit-learn
- classification
Marketing Analytics Specialist
Translating churn predictors into retention actions for a non-technical team is the heart of marketing analytics. The challenge strengthens the ability to turn model output into campaign decisions that move retention metrics.
This challenge sharpens
- feature-engineering
- logistic-regression
- classification