Developer Builds Telecom Churn Prediction Model and Deploys It as a FastAPI Service

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A developer has shared a walkthrough of building a customer churn prediction API using the Telco Customer Churn dataset, which contains records of 7,043 telecom customers across 21 columns. The project involved data cleaning, exploratory data analysis, and training three classifiers — Logistic Regression, K-Nearest Neighbors, and Random Forest — before selecting the best performer. The final model, along with its preprocessing pipeline, was saved using joblib and deployed as a REST API built with FastAPI. The goal was to bridge the gap between a trained notebook model and a usable service that frontend applications or business tools can query for real-time predictions. The project is open-sourced on GitHub and aimed at developers who have trained ML models but are unsure how to make them accessible beyond a Jupyter notebook.

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