Churn prediction helps businesses identify customers at risk of leaving and enables proactive retention strategies.

describe what can people use it for, or how it makes existing tasks easier/safer e.t.c (markdown supported)

The problem it solves

tell us about any specific bug or hurdle you ran into while building this project. how did you get over it? (markdown supported)

Challenges we ran into

Datathon-2025

React

Flask

random forest

catboost

PowerBI

NoChurn

Our project seamlessly aligns with the Core ML track by leveraging machine learning methodologies to address real-world challenges in customer retention and churn prediction. By utilizing predictive models like Random Forest and CatBoost, combined through ensemble learning, we effectively analyze historical customer data to predict churn with high accuracy and robustness. Extensive preprocessing, feature engineering, and hyperparameter optimization ensure the model's performance is both reliable and scalable. Additionally, the project incorporates explainable ML techniques, such as feature importance analysis and SHAP values, to provide transparency and actionable insights into the factors driving churn, like contract type, monthly charges, and additional services. Designed for real-world business applications, the system empowers organizations to proactively reduce churn, enhance customer lifetime value, and implement targeted retention strategies. The trained models are easily deploy

Core ML

Cyborgs

NoChurn

Prevent. Predict. Retain.

NoChurn

Prevent. Predict. Retain.

The problem NoChurn solves

Challenges we ran into

Core ML

Discussion

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