{"data":{"full_name":"HaiHuynh206/Lead_scoring_model","name":"Lead_scoring_model","description":"In this project, I leverage machine learning models including Logistic Regression, Decision Tree, Random Forest, XGBoost, CatBoost, and LightGBM to predict customer lead scoring. I apply WOE and SHAP for feature selection and use Optuna for hyperparameter turning, aiming to identify potential lead customers effectively.","stars":7.0,"forks":0.0,"language":"Jupyter Notebook","license":null,"archived":0.0,"subcategory":"bank-deposit-prediction","last_pushed_at":"2024-04-19T13:04:16+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":0.0,"adoption_score":4.0,"maturity_score":8.0,"community_score":0.0,"quality_score":12.0,"quality_tier":"experimental","risk_flags":"['no_license', 'stale_6m', 'no_package', 'no_dependents']"},"meta":{"timestamp":"2026-04-11T13:28:04.157945+00:00"}}