HR Insight Analytics: An Intelligent Platform for Employee Performance Evaluation and Attrition Risk Prediction using Random Forest and Workforce Visualization

Pasupuleti Sai Deepthi, Mr. S. Manjunath Reddy

Employee Attrition Prediction; HR Analytics; Machine Learning (Random Forest); Workforce Performance Analysis; Predictive Modeling.

HR Insight Analytics is an end-to-end platform designed to predict employee performance and attrition risk, addressing the high costs and disruptions caused by unexpected workforce turnover. Attrition is influenced by multiple factors, including compensation dissatisfaction, limited career growth, poor work-life balance, workload stress, and weak managerial relationships. The platform leverages a synthetic dataset of 15,000 employee records with 34 attributes covering demographics, job roles, compensation, engagement levels, and attendance patterns. The system operates through four stages: a Python-based engine generates realistic workforce data; a Random Forest classification model with advanced feature engineering predicts attrition with high accuracy using cross-validation; data is structured in a normalized MySQL database for efficient storage and querying; and interactive dashboards present insights on attrition trends, performance distribution, and risk factors. The platform delivers actionable insights by assigning attrition probabilities, risk categories, and retention priorities to employees, along with tailored intervention strategies, enabling organizations to proactively manage workforce challenges and improve retention outcomes.

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