# Predicting Employee Attrition Using Machine Learning Approaches
## 2 Related Work  
The related literature to our research study is examined in this section. The related literature is based on the summary of past applied approaches and research outcomes for predicting employee attrition. The most recent applied state-of-the-art approaches were selected for the literature review. In the classification task, the Performance Assessment of Data Balancing Techniques was proposed [11]. The imbalanced dataset causes a major issue in numerous classification problems such as intrusion detection, fraud detection, anomaly detection, and many more. The data balancing was applied to obtain the high accuracy results from prediction models. The research study addresses the performance issues empirically using an imbalanced dataset. Hybrid Sampling (HS), Synthetic Minority Over Sampling (SMOTE), Under Sampling (US), Random Over Sampling Examples (ROSE), Over Sampling (OS), and Clustering-Based Under Sampling (CBUS) balancing techniques were examined. The imbalance ratio (IR) was used as the performance factor. The research experimental results show that data balancing proves helpful in improving the applied classifiers' performance. The results also indicate that no significant performance difference was found in US, SMOTE, HS, OS, and CBUS balancing techniques. The introduction and real-world applications of neural network techniques were analyzed [12]. Neural networks are a subtype of the machine learning area. The advantages of neural network techniques are high-speed processing and parallelism with big data. Neural networks are useful and novel techniques for solving learning problems. The working of neural networks is similar to the biological nervous systems of the human brain. The major brain element is the information processing unique design. This is based on the numerous complex neurons that are interconnected. The neural networks contain layers of nodes that are independent of one another. The study analyzed the neural network techniques, performance comparison, and challenges. The research study presented that the feedforward and feedback propagation neural networks techniques were performing better with the huge dataset to solve real-world problems. The analyzed factors were fault tolerance, processing speed, accuracy, latency, scalability, volume, and performance. The prediction of employee attrition rate using machine learning-based classification algorithms was proposed [13]. The HR employee data collected from Kaggle were utilized for the model-building process. The K-Nearest neighbors, extreme gradient boosting, Ada Boosting, Decision Tree, neural networks, and Random Forest applied machine learning techniques [14] for the classification task. The regularization techniques were applied to find the best-fit parameters for predicting the employee attrition rate. The different steps were applied to obtain an accuracy score of 88%. The automated prediction of employee attrition based on several machine learning models was proposed in this study [15]. The IBM HR employee dataset was utilized for learning model building and model evaluation process. The Ad boost Model, Random Forest Regressor, Decision Tree, Logistic Regressor, and Gradient Boosting Classifiers were utilized for the prediction task. The Decision Tree and Logistic Regressor achieved an 86% accuracy score. The goal was the accurate detection of employee attrition to help the organizations to boost their employee satisfaction [16]. The three-stage system based on preprocessing, processing, and post-processing techniques was proposed to predict employee attrition [17]. The IBM HR employee dataset was utilized for framework training and testing. The max-out feature selection technique was utilized for the dimension reduction stage. The logistic regression technique was utilized for employee attrition prediction. The model results achieved an 81% of accuracy score. The framework parameters were validated.