Employee Attrition Prediction in Industries using Machine Learning Algorithms

Employee attrition is a major issue among all employee-related issues in the current setting, notwithstanding changes in the external environment. Attrition is defined as a gradual decrease in the number of employees due to seclusion, death, renunciation, and resignations. This research employs logistic regression to forecast whether or not a firm employee will leave. We evaluate job involvement, average monthly hours worked, years employed, gender, education level, and job satisfaction, among other things, as our features. K-nearest neighbors (KNN), Support Vector Machine (SVM), Random Forest (RF), and decision trees are more methods for solving this issue. The dataset was divided, with 80% of it being used to train the algorithm and 20% of it being used to test it, yielding a Logistic Regression (LR) accuracy of 88.0952%.