Implementation of Machine Learning Model for Employee Retention Prediction
Keywords:
Attrition Rate, HR, Classifier, Preprocessing, Employment FeaturesAbstract
Employees are regarded as the organization's backbone. The personnel who work for it determine the success or failure of a company. When trained, competent, and experienced personnel depart for greater opportunities, businesses must deal with the resulting challenges. The purpose of the research was to determine the level of employee unhappiness and the reasons why they would choose to change jobs. Once the dissatisfaction factor(s) of employees has/have been discovered, businesses can take appropriate action, which may aid in lowering the turnover rate. In this research, we attempt to develop a system that can forecast employee attrition using data from the Kaggle website's Employee dataset. To visualise the relationships between the attributes, we created a heatmap. We employed four different machine learning methods for prediction, including KNN (K-Nearest Neighbor), SVM (Support Vector Machine), Decision Tree, and Random Forest. The reasons for employee attrition in any firm are discussed in this study.
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