Efficient Data Mining Model for Employees Churn Prediction and Safety Measure

Abstract

Employee Churn which is otherwise called representative turnover is an exorbitant issue for organizations. The genuine expense for supplanting a worker can frequently be very enormous. In this work, we aimed to understand why and when employees are most likely to leave a company i.e the probability of an active employee leaving the organization and the key factors of an employee leaving the organization. For this purpose, we created such standard dataset where we include those attributes that are helpful for our analysis to predict the factors that are responsible for an employee to leave a company. The attributes we used in the dataset are satisfaction level, last evaluation, a number of projects, monthly average hours, amount of time spend in the company, employees left the company, promotions in last 5years, departments, salary. Further, under these attributes, we include 603 data samples. It is also useful to the company to retain the employees' safety and secure without losing them in the organization for a long time. We applied various Machine Learning models such as, Logistic Regression Classifier, Random Forest Classifier, SVM to check that our dataset is resulting with accurate values or not and which model is predicting the best. Thus, after applying all the models to the dataset, the Random Forest Classifier is giving more accuracy that is about 97.2% when compared to all the other classification models. This Random Forest Classifier correctly depicts the factors responsible for an employee leaving the company.