ML Application · Jupyter Notebook

Churn-prediction

kamran akmal@kamranakamal · Atlas 61.6 · #19= builder

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What it is

This repository provides an end-to-end machine learning application for customer churn prediction, including model training, a FastAPI inference server, and a Streamlit user interface. It uses standard libraries such as scikit-learn, XGBoost, and pandas for data processing and modeling.

Scored at4185b1c9 Nov 2026Changes since

Merit describes the code at this commit. Later pushes count once the owner rescans. How merit is scored

Where it counts

Machine Learning90%
Data Science70%
MLOps50%

How much of the project is about each field, as scoring counts it. It ranks in the field it is most about.

What the judge found

Read from the project's code at its scored commit.

Strengths4

End-to-end application covering data processing, model training, serving, and user interface.
Clear separation of concerns with modular scripts for training, API, and UI.
Use of standard and well-established ML libraries (scikit-learn, XGBoost) for reliability.
Includes containerization via Docker for deployment.

Limitations

The owner keeps this project's limitations private.

Around it on the Machine Learning board

Whole board

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