Overview
What this challenge is about.
Train three models on chronological student data, deliver a ranked churn list and memo, earn a verifiable certificate.
The scenario
The startup (around 40 staff, around 9,000 paying families, EUR 22 average monthly revenue per user) sees churn as the #1 lever before raising a Series A in mid-2027.
The Brief
What you'll do, and what you'll demonstrate.
Build a churn-prediction model that gives Customer Success a usable ranked list of at-risk students 30 days before cancellation.
Earning criteria — what you'll demonstrate
- Apply supervised learning to a real tabular business problem
- Choose appropriate evaluation metrics for an imbalanced classification task
- Use regularization and cross-validation to avoid overfitting
- Communicate model behaviour to a non-technical stakeholder
Program Fit
Where this fits in your program.
Sharpens the same skills your degree expects you to demonstrate.
Machine Learning (Undergraduate)
Bachelor · Machine Learning
Strong alignment
This challenge maps to Machine Learning (Undergraduate) at the Bachelor level. It sharpens the same practical skills your coursework expects — but in a real industry context with actual constraints and deliverables.
Skills
Skills you'll demonstrate.
Each one shows up on your verified credential.
- Supervised Learning
Apply supervised learning 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.
- Gradient Boosting
Apply gradient boosting to solve real industry problems and demonstrate production-level capability.
- Model Evaluation
Apply model evaluation 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.
- Python
Write clean, efficient Python for data processing, automation, and backend services.
Careers
Career paths this challenge builds toward
Completing this challenge demonstrates skills that transfer directly to these roles:
Data Scientist
Framing a business problem, choosing the right evaluation metric, and shipping a ranked list a non-technical team will use is the day-one job description of a junior data scientist at any subscription business.
This challenge sharpens
- supervised-learning
- model-evaluation
- feature-engineering
Machine Learning Engineer
Wrapping preprocessing and a trained model into a reproducible pipeline is the first step toward shipping an ML system into production.
This challenge sharpens
- python
- gradient-boosting
- feature-engineering
Applied AI Scientist
Comparing model families with calibrated, leakage-free evaluation is the bread-and-butter of applied AI work at product-led startups.
This challenge sharpens
- logistic-regression
- gradient-boosting
- model-evaluation