Employee Attrition Predictor: AI-Powered HR Analytics System for Employee Retention

Employee attrition poses a significant financial challenge for organizations, with turnover costs estimated at up to 200% of an employee’s annual salary when accounting for recruitment, onboarding, and lost productivity. Traditional HR approaches rely on reactive measures, often identifying flight risks only after resignation. This paper presents a machine learning-based predictive system designed to proactively identify at-risk employees and support data-driven retention strategies. The proposed system was developed and evaluated on a dataset of 1,470 employee records comprising 12 behavioral, demographic, and organizational features. Three supervised classification algorithms were implemented and compared: Logistic Regression, Random Forest, and Decision Tree. The pipeline incorporates automated data validation, categorical feature encoding, numerical normalization, and a risk scoring module for business impact quantification. Statistical analyses including chi-square tests and Pearson correlation were conducted to identify significant attrition drivers. The system was deployed as an interactive web application using Streamlit with Docker containerization, making it accessible to non-technical HR stakeholders. This work demonstrates that interpretable machine learning models, combined with business intelligence modules, can deliver actionable and measurable value in workforce management.