Exploring Employee Working Productivity: Initial Insights from Machine Learning Predictive Analytics and Visualization

Abstract

Employee working productivity prediction is vital for effective resource allocation, increased productivity, and upholding a high-performance culture in organizations. However, predicting employee productivity and understanding the root factors influencing working performance pose significant challenges. Traditional human resource management practices often lack data-driven insights, resulting in poor resource allocation and productivity enhancement strategies. Subjective assessments, numerous assessment factors, and difficulties in interpreting predictive mechanisms add to the complexity of the task. To address these challenges, this research aims to develop a predictive model using machine learning techniques to determine employee productivity within organizations. Data from an academic institution were collected and pre-processed by encoding relevant features before applying various machine learning predictive models. Decision tree regressor, linear regression, MLP regressor, random forest regressor, SGD regressor, voting regressor, and Xgboost regressor were employed as predictive models. Ranker algorithms, including InfoGainAttributeEval, GainRatioAttributeEval, and CorrelationAttributeEval, were utilized to identify the most significant attributes affecting employee working performance. Additionally, descriptive analytics techniques were employed to visualize the data, extracting valuable insights and understanding the correlations among the features. Experimental results revealed that the linear regression model achieved the best performance in terms of Mean Absolute Error (MAE) and Mean Squared Error (MSE), with values of 0.4878 and 0.4682, respectively. Thus, the linear regression model emerged as the most accurate predictor for employee productivity in the given organizational context. Based on these findings, it is recommended that organizations consider adopting linear regression for predicting employee productivity. The research findings also highlighted certain attributes that play an imperative role in predicting employee performance. Attributes such as "Department," "Actual Productive hours," "Internet Speed," and "COVID-19 adoption month" emerged as highly influential factors across multiple ranking techniques. The data visualization provided valuable insights into various aspects of employee performance, such as productivity trends before and after the pandemic, departmental performance, internet connectivity's impact on productivity, age-related trends, overtime distribution, and promotion rates. Organizations can use this data to inform workforce planning, address specific challenges in departments, and cultivate an inclusive work environment. By regularly assessing productivity data and implementing recommended strategies, organizations can enhance productivity, create a conducive work environment, and support employee well-being and growth. Future research can explore more advanced machine learning algorithms, incorporate time-series analysis for temporal dependencies, and expand data collection from diverse organizational settings to improve the generalizability of predictive models.