shantanu1109/IBM-HR-Analytics-Employee-Attrition-and-Performance-Prediction
In this project, we enlisted the numerical and categorical attributes present in the publicly available dataset. Missing values were dropped to give better insights in data analysis. ANOVA and Chi-Square tests were carried out during statistical analysis. Machine Learning algo's were applied to understand, manage, and mitigate employee attrition.
Implements an ensemble approach comparing seven classifiers (Logistic Regression, Random Forest, SVM, XGBoost, LightGBM, CatBoost, AdaBoost) with ROC curve visualization using hvPlot, leveraging scikit-learn, XGBoost, LightGBM, and CatBoost libraries. The pipeline performs one-hot encoding of categorical features via pandas `get_dummies()`, trains/test splits via scikit-learn, and evaluates models using accuracy scores and confusion matrices. Targets the IBM HR Analytics dataset (1,470 records, 35 attributes) to identify attrition risk factors through statistical testing and predictive modeling.
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Oct 05, 2023
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