Analysis of Employee Attrition using for Machine Learning Techniques
Main Article Content
Abstract
Nowadays, the daily forecast for employee losses becomes a major issue. Staff participation is an important issue for the organization, especially when professional technical staff and key people in the organization come from good positions. This leads to a loss of finances to replace skilled labor. Therefore, we use data from current and former employees to analyze common causes of employee access or influence. To avoid employment, we use several planning methods, namely: Decision Tree, Log-log of Backlog, SVM, KNN, Random Forest, Bayes Naive. To do this, we use the method to select employment information and analyze the results to avoid employee income. Companies need to anticipate employee incentives and contribute to economic growth by reducing manpower.
Downloads
Metrics
Article Details
Licensing
TURCOMAT publishes articles under the Creative Commons Attribution 4.0 International License (CC BY 4.0). This licensing allows for any use of the work, provided the original author(s) and source are credited, thereby facilitating the free exchange and use of research for the advancement of knowledge.
Detailed Licensing Terms
Attribution (BY): Users must give appropriate credit, provide a link to the license, and indicate if changes were made. Users may do so in any reasonable manner, but not in any way that suggests the licensor endorses them or their use.
No Additional Restrictions: Users may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.