Employee Engagement and Turnover utilizing Logistic Regression

In this paper, we introduce the application of a profiling system to suggest appropriate employee profiles for project assignments based on task specifications. The primary objective of this system is to assist managers in gaining a comprehensive understanding of their employees’ profiles and motivations. Our research introduces a recommendation system that relies on a profiling approach, analyzing messages and publications shared within a professional network. The proposed system is composed of two main components. The first component focuses on profiling, extracting relevant information from the company’s Human resources (HR) data, identifying interests, and establishing a psychological profile from publications exchanged within the professional platform. The second component is dedicated to recommending profiles that closely align with the specific requirements of each project. Our system yields promising results in predicting favored candidates for projects, achieving an accuracy of 0.92 and an F-score of 0.94. By integrating message-based profiling and leveraging data from professional networks, our approach proves to be effective in recommending well-suited candidates for various projects.

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Employee Engagement and Turnover utilizing Logistic Regression

Cited by 3 publications

References 13 publications

Estimation of Employee Engagement in Organisations during Crisis using Machine Learning Technique

Estimation of Employee Engagement in Organisations during Crisis using Machine Learning Technique

Workforce Data Analysis

Using a Profiling System to Recommend Employees to Carry out a Project

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