{"data":{"full_name":"dyneth02/FDM-Labs","name":"FDM-Labs","description":"Comprehensive machine learning framework for genomic analysis and predictive modeling. This repository showcases advanced classification and clustering techniques using XGBoost, CatBoost, LightGBM, and RandomForest to identify genetic disorders. Includes association rule mining with Apriori and unsupervised geographical clustering via KMeans.","stars":8.0,"forks":0.0,"language":"Jupyter Notebook","license":"MIT","archived":0.0,"subcategory":"genomic-variant-analysis","last_pushed_at":"2025-12-25T19:16:02+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":6.0,"adoption_score":4.0,"maturity_score":9.0,"community_score":0.0,"quality_score":19.0,"quality_tier":"experimental","risk_flags":"['no_package', 'no_dependents']"},"meta":{"timestamp":"2026-04-06T00:08:26.982147+00:00"}}