Machine learning model to predict employees turnover using Python

Machine learning, a rapidly advancing subfield of artificial intelligence, enables computer systems to learn from data and adjust their behavior accordingly. Its core objective is to develop models capable of making predictions, executing tasks, and improving performance over time. One effective and impactful application lies in predicting employee turnover. By utilizing both historical and current employee data—such as job performance, demographic factors, and job satisfaction—predictive models can be developed to identify individuals at a higher risk of leaving the organization. This data-driven approach provides valuable insights into workforce dynamics and supports the creation of targeted employee retention strategies. The choice of the most suitable machine learning algorithm for turnover prediction depends on several factors, including the specific nature of the problem, the size and quality of the dataset, and the required level of model interpretability. While some algorithms perform well with large and complex datasets, others are more effective in limited or domain-specific contexts. Once trained, the model can generate accurate predictions of employee behavior, allowing organizations to take proactive measures to reduce attrition. This research aims to implement and evaluate various machine learning algorithms using Python in order to identify the most effective model for predicting employee turnover based on empirical data.