Learn by Directing AI

The Brief

Emeka Okafor runs customer retention at Tunde Mobile, an MVNO in Lagos. They lose 2–3% of subscribers every month. His retention team makes calls, but right now they're guessing who to call.

Emeka has twelve months of subscriber data and a clear ask: a model that predicts who will leave, ranked by risk, served as an API his team can query weekly. He sent an email. The data is clean. The requirements are specific.

There's one thing worth noticing early. Only about 8% of subscribers in that dataset actually churned.

Your Role

You're building Emeka's churn prediction system. You direct Claude Code through the entire workflow — from profiling the data to serving a live API endpoint. AI writes the code. You provide direction, verify the results, and communicate back to Emeka.

This is the first real project. Everything you need is provided: the brief, the dataset, the algorithm, the evaluation criteria, the ticket breakdown. Your job is not to decide what to build. Your job is to direct AI accurately and verify that what it produces actually works for Emeka's problem.

What's New

Setup gave you the tools. This is where you use them for real work.

What changes is the shape of the work itself. You'll move through a full ML workflow — data profiling, preprocessing, training, evaluation, serving — directing AI through each stage. Every stage produces something concrete you can check against specific criteria.

Tools

Python / pandas — data loading and profiling
scikit-learn — preprocessing, training, evaluation
Jupyter — notebook workflow
FastAPI / uvicorn — model serving
Claude Code — AI direction
Git / GitHub — version control
curl — API testing

Materials

Everything is provided for this project:

Emeka's email with the full brief
A clean subscriber dataset
A data dictionary describing every column
Algorithm and evaluation criteria (RandomForest, recall ≥ 0.55 on the churn class)
A complete ticket breakdown for every stage of the work

You won't need to decide what to build or how to structure the work. The materials handle that. Focus on directing AI through each ticket and verifying what it produces.