학술논문

Analyzing Employee Retention Factors using Machine Learning
Document Type
Conference
Source
2024 Fourth International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies (ICAECT) Advances in Electrical, Computing, Communication and Sustainable Technologies (ICAECT), 2024 Fourth International Conference on. :1-7 Jan, 2024
Subject
Communication, Networking and Broadcast Technologies
Components, Circuits, Devices and Systems
Computing and Processing
Engineered Materials, Dielectrics and Plasmas
Fields, Waves and Electromagnetics
General Topics for Engineers
Photonics and Electrooptics
Power, Energy and Industry Applications
Robotics and Control Systems
Signal Processing and Analysis
Productivity
Ethics
Costs
Machine learning algorithms
Software algorithms
Companies
Machine learning
Data Science
Machine Learning
Deep Learning
Smartphone Sensors
Artificial Intelligence
Monitoring
Language
Abstract
This research proposal aims to employ machine learning techniques to analyze employee retention factors in Software Companies, recognizing its crucial role in organizational success and the potential costs of high turnover rates. Through Watson Analytics' advanced analytics capabilities, the study seeks to identify key factors contributing to employee attrition and retention, culminating in the development of a predictive model using machine learning algorithms. The outcomes are expected to include actionable recommendations to improve retention strategies, insights into the relative importance of different factors, and the creation of a data- driven, proactive talent management approach for Software Companies. By empowering organizations to retain top talent and fostering a positive work environment, this research envisions a transformative impact on long-term success and performance.