Throughout the course of careers, a large number of employees encounter a variety of normal life events that can have an impact on their performance and job environment satisfaction. Under these circumstances, some employees are compelled to leave their positions, not because of issues with their work environment, but due to personal circumstances. This is what we refer to as Employee Attrition, a pervasive phenomenon affecting numerous businesses and work environments in the present day. Employee attrition could occur in a variety of ways and be determined by several entities. Therefore, we look into various conditions, causes, indicators, and entities in order to study the attrition problem and determine its answers and enhancements. This study investigated the attrition of employees based on IBM data repository, together with the machine learning based experimental analysis and knowledge discoveries, to identify the most effective approaches to the attrition problem. The experimental data contained 1,471 records with 35 distinct characteristics. We formally employed Nave Bayes Classifier in this study using WEKA datamining software, and adequate knowledge has been extracted to better comprehend employee turnover and their work performance facets.
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