Employee attrition prediction model in the airline industry: Utilizing Machine Learning
Employee attrition is a major concern today, as companies are facing it, especially after the coronavirus pandemic, due to the high volume of turnover and the need for employees to work from home. In 2025, in many big IT companies, employee attrition rates were close to 20%, which is very high. Therefore, since this is a critical issue, steps need to be taken to reduce it and make employees more comfortable in their current workspaces. It is essential for companies to figure out the top reasons why employees are leaving, along with the remedies for such reasons. A real-life dataset is taken from an analytics firm, which consists of data of 1470 employees distributed over 35 features. Out of these 35 features, the top reasons are separated as to why employees have left, which were 11. An analysis of these 11 reasons is done. Then model building is done, where 7 machine learning classification algorithms are run, in which the highest test data accuracy was given by logistic regression (87.23%). Based on this, a predictive system was built that would tell whether a particular employee will leave the company or not, thus solving many issues in the IT department of airlines.
Keywords: Machine learning, airline IT department, business management, aerospace sector
