Analyzing Attrition and Performance of an Employee using Machine Learning Techniques

Attrition is described as the progressive drop in the number of employees in a company without replacing them. Attrition rate being high is unfavorable for many reasons, it leads to loss of talent, poor research, and resources spent on training employees. The major goal is to use advanced Machine Learning techniques like Random Forest Classifier, TabNet, Gradient Boosting Classifier, and MLP (Multi-Layer Perceptron) Classifier to solve the attrition problem. MLP outperformed all other models with 0.88 accuracy, 0.116 Hamming Loss, 0.393 Jaccard Score, and 3.994 Log Loss after being trained using the dataset. As a result, MLP accurately anticipated the attrition value of individuals in the business and assisted the management in making crucial personnel decisions.