EMPLOYEE SATISFACTION SCORE PREDICTION (HR ANALYTICS)

Abstract

Employee satisfaction is an important factor that influences workplace productivity, employee engagement, retention, and organizational performance. Organizations collect information related to workload, salary, working hours, job role, experience, training, performance, work environment, and employee feedback. Analyzing these factors can help organizations understand employee satisfaction patterns. The Employee Satisfaction Score Prediction system is designed to analyze employee-related information and predict satisfaction scores using historical HR data. The system processes attributes such as department, job role, salary, experience, working hours, workload, performance rating, training participation, overtime, and satisfaction-related factors. Data preprocessing techniques are applied to handle missing values, duplicate records, inconsistent formats, and invalid data. The system performs exploratory analysis to identify relationships between employee characteristics and satisfaction levels. Important indicators such as average satisfaction score, department-wise satisfaction, role-wise satisfaction, workload impact, salary-related patterns, and work-environment factors can be analyzed. Interactive dashboards present these insights through KPI cards, charts, graphs, tables, and filters. The prediction module uses Machine Learning algorithms to estimate an employees satisfaction score or classify satisfaction into categories such as low, medium, and high. Features such as workload, working hours, salary, performance, training, experience, and other available HR attributes can be used as predictive inputs. Model evaluation metrics are used to assess prediction performance. Overall, the proposed system combines HR analytics, employee satisfaction analysis, visualization, and predictive modeling into a centralized platform. It can help organizations understand historical satisfaction patterns and identify factors associated with employee experience. Future enhancements can include employee feedback sentiment analysis, real-time survey integration, attrition-risk analysis, personalized workplace recommendations, and continuous satisfaction monitoring.