EmpTurnoverML: An Efficient Model for Employee Turnover and Customer Churn Prediction Using Machine Learning Algorithms

Employee turnover and customer churn a significant challenges for organizations worldwide. The loss of employees can significantly impact the company's growth and success, from decreased productivity to increased costs, including recruiting and training new employees. To mitigate this problem, many organizations are adopting artificial intelligence (AI) techniques to detect employee churn. In this approach, AI algorithms like KNN, Decision Tree, Logistic Regression, Random Forest, SVM (Support Vector Machines), ADA boost, Naïve Bayes, and GBM(Gradient Boosted Machine Tree) are trained on historical data. The data set was divided into two segments, with 80% allocated for training and the rest 20% for testing purposes to identify patterns and predict which employees are likely to leave the organization. By doing so, companies can take proactive measures to retain their employees and reduce the costs of hiring and training new ones. Using AI to detect employee churn can potentially provide significant benefits for companies, including improved retention rates, increased productivity, and reduced costs. Overall, using AI to detect employee churn is a promising solution that can help companies retain their employees and achieve their business goals.