{"data":{"full_name":"RaghavGoel822/Costumer_Churn_Prediction","name":"Costumer_Churn_Prediction","description":"This is a customer churn prediction project using machine learning algorithms like Logistic Regression, Random Forest, K-Nearest Neighbors, Support Vector Machine, XGBoost, and Gradient Boosting. The project aims to analyze and predict customer churn in a dataset, using techniques like class weighting and SMOTE to handle class imbalance","stars":0.0,"forks":0.0,"language":"Jupyter Notebook","license":null,"archived":0.0,"subcategory":"customer-churn-prediction","last_pushed_at":"2026-03-26T06:29:58+00:00","pypi_package":null,"npm_package":null,"downloads_monthly":0.0,"dependency_count":0.0,"commits_30d":null,"reverse_dep_count":0.0,"maintenance_score":13.0,"adoption_score":0.0,"maturity_score":1.0,"community_score":0.0,"quality_score":14.0,"quality_tier":"experimental","risk_flags":"['no_license', 'no_package', 'no_dependents']"},"meta":{"timestamp":"2026-04-07T00:30:27.485407+00:00"}}