Comparative Study of Predictive Models to Estimate Employee Attrition

Employees put in a lot of effort to make the company prosperous. As a result, this research work attempts to construct a model that, utilizing HR analytics data provided from the Kaggle platform, can forecast employee attrition rate in this research. Attrition proves to be a costly and time-consuming problem for the organization, and it also leads to loss of productivity. The relationship among attributes is shown using correlation matrix and heatmap. The histogram, which depicts the disparity between left and right employees, is constructed in the experimental portion in terms of income, department, level of pleasure, and so on. The proposed research employs four distinct machine learning techniques for prediction, including Logistic Regression, Random Forest, Naive Bayes classifier and K-Nearest Neighbor. This document proposes the factors that help an organization’s staff turnover rate to be as low as possible.