Overview
What this challenge is about.
Predict Subscription Churn for an EdTech Platform. Intermediate challenge in code. Writing production code that solves real engineering problems, earn a bloc...
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.
This is not a coding exercise. It is the work a software engineer does between a Jira ticket and a merged PR. That distinction matters to every hiring manager who has seen candidates solve LeetCode problems and none who have shipped production code under real constraints.
When you finish, you will have something most graduates do not: a real-world deliverable, verified by Ewance, that you can show to a hiring manager and say "I did this. Here is the proof."
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