An end-to-end employee attrition prediction project built on the IBM HR Analytics dataset, where multiple machine learning models were trained, compared, and optimized to predict employee turnover. The project also uses SHAP for explainability and survival analysis to understand the key factors influencing attrition and employee retention.

The Employee-Attrition-Predictor focuses on predicting employee attrition using machine learning techniques. It is likely inspired by the IBM HR Analytics Employee Attrition & Performance dataset, a widely-used dataset in the field of HR analytics and employee retention.

This project focuses on predicting employee attrition using machine learning techniques. The goal is to identify employees at risk of leaving the organization by analyzing factors such as job satisfaction, workload, tenure, and promotions.

This project focuses on predicting employee attrition using machine learning techniques. It analyzes factors influencing employee turnover, such as job satisfaction, work environment, and compensation. By leveraging data preprocessing, feature selection, and classification models, the project provides insights for retaining valuable talents.