INX-Employee-Performance-Analysis
PublicEmployee performance prediction using XGBoost multiclass classification (92.5% accuracy, 93.3% CV F1-score) with SHAP interpretability. Analyzes 1,200 employee records across 28 features, identifies top 3 performance drivers, and provides HR recommendations. Full pipeline: EDA, feature engineering, model comparison, and deployment-ready inferen
catboostclassificationdata-analysisdata-scienceemployee-performancefeature-engineeringhr-analyticsjupyter-notebooklightgbmmachine-learning
Creat：2025-04-08T22:48:54
Update：2025-11-11T03:17:04
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