HR Attrition Prediction System
A supervised machine learning system that predicts employee attrition and provides actionable insights through both predictive modeling and an interactive analytics dashboard.
A supervised machine learning system that predicts employee attrition and provides actionable insights through both predictive modeling and an interactive analytics dashboard.
Organizations struggle to identify employees likely to leave, leading to unexpected attrition and increased hiring and training costs.
Built a machine learning model that predicts attrition risk and complements it with visual dashboards for easier decision-making.
Kaggle HR dataset with ~4,190 records and 41 features including performance, satisfaction, and job-related attributes.
Built a full preprocessing pipeline using ColumnTransformer for encoding, scaling, and feature transformation.
Optimized for Recall to minimize missed attrition cases and ensure better business impact.
Achieved ~79–80% recall using Logistic Regression with class imbalance handling.
Model deployed using Streamlit with a user interface for real-time predictions. Model saved using Pickle/Joblib for integration.