# Predicting Employee Attrition Using Machine Learning Approaches
## 2 Related Work
The comparison of state-of-the-art machine learning methods was applied to predict employee attrition using the IBM HR employee dataset [18]. The results of the study were utilized to warn managers to update their business strategies [19]. The six machine learning models were utilized. The Random Forest was the proposed approach. The accuracy of the proposed approach was 85% for the prediction of employee attrition. The study findings are based on the factors social, financial, cultural, relational, and professional that caused employee attrition. The prediction of employee attrition using the IBM HR employee dataset was proposed [20]. The seven-machine learning techniques were applied and evaluated. The factors that cause attrition were determined by the gain ratio approach. The dataset balancing was applied using bootstrapping technique. The proposed model achieved an 80% of accuracy score. The factors that cause attrition were ranked according to their gain ratio scores. The employee attrition prediction using a machine learning pipeline [21] was proposed in this study [22]. The study findings analyze the factors such as the number of years of work experience, educational qualifications, gender, and department were that caused employee attrition. The pipeline was based on gradient boosting and ensemble learning techniques. The hyperparameter tuning was applied to models using a randomized grid search technique. The pipeline algorithm achieved state-of-the-art performance. The k-fold cross-validation was applied for model evaluations. The emotional assessment and prediction of employee attrition rate of the employee were proposed [23]. The dataset was collected through a survey based on attrition-related questions. The Decision Tree, Random Forest, and Support Vector Machine classifiers were applied for the prediction task. The proposed approach achieved an 86% of accuracy score in predicting attrition rate. The systematic flow for predicting employee attrition using machine learning techniques was proposed in this research study [24]. The machine learning models Naive Bayes, Random Forest, Decision Tree, Support Vector Machine, and K-Nearest Neighbor were applied using the python tool. The Random Forest was the proposed approach with an accuracy score of 83%. The key causes of employee attrition were found and minimized using data analysis. The rise of a sudden pandemic in 2020 brought significant losses in employment to the global economy [25]. In China, a governmental force contributed to 50% of tax revenue, 80% of jobs, 60% of GDP, and 70% of innovation. The limited capability results in a higher employee attrition rate. The higher employee attrition rate causes unemployment stress. The studies show that job stress cause problems of job dissatisfaction and burnout, resulting in a higher turnover rate. The related literature was examined in comparison with our proposed study. The analysis demonstrates that our proposed model outperformed in comparison with machine learning techniques and state-of-the-art studies. We applied hyperparameter optimization and data balancing techniques to achieve the highest accuracy scores for the prediction of employee attrition. The literature analysis concluded that these applied techniques were not performed by past applied techniques in the related literature.