Train a classifier on customer data and serve its predictions behind a small HTTP endpoint.
The business wants to know which customers are likely to leave, but the signal is buried in a table nobody scores.
A trained classifier turns historical behaviour into a probability per customer, so the team can act on the highest-risk accounts first.
Use this for churn, lead scoring, or any yes/no prediction where you have labelled history.
Python, scikit-learn, and a labelled CSV (features plus a churned 0/1 column).
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The step by step build and conclusion are part of a ByteLabs bundle. Enrol once to unlock every gated section in it for 3 months.
Enrol in "AI, ML and DSA Realtime Starter" · ₹589Already bought this? Sign in to open it.