Employee churn, or turnover, is a major concern for organizations as it leads to decreased productivity, increased hiring costs, and loss of knowledge and expertise. This project explores the application of machine learning techniques to predict employee churn based on historical data and various relevant features. This model can assist Human Resources (HR) professionals in identifying high-risk employees, enabling proactive retention strategies. The project will utilize a comprehensive dataset of employee records, including demographic information, performance metrics, job satisfaction surveys, workplace factors, and other relevant factors. The methodology involves data preprocessing techniques, feature engineering, and the application of various classification models to accurately forecast employee churn. Exploratory data analysis is conducted to gain insights into the patterns and correlations within the dataset. Several machine learning algorithms are implemented and compared to identify the most effective model for employee churn prediction. In addition to predicting churn, the project will also investigate the relative importance of various features in the churn prediction model. This model will provide insights into the key factors driving employee churn and allow organizations to focus their retention efforts on addressing these factors.