Employee attrition is an important factor that can affect organizations, both financially and operationally. Human Resource (HR) managers often find it difficult to identify exactly which employees might be planning to leave the organization and what is the root cause for their decision. With the recent advances in computing, Machine Learning (ML) techniques are available for analysing, understanding, and solving complex problems. This study analyses the IBM HR Analytics dataset using ML techniques to predict employee attrition and identify the key factors that influence attrition. Four ML models based on Logistic Regression, Decision Tree, Random Forest, and Gradient Boosting have been used for analysing attrition. It is found that Logistic Regression outperformed all other models in predicting attrition. At the same time, Decision Tree is found to be the weakest among the four techniques. On the analysis of feature importance, it is found that variables related to compensation (Monthly Income), career stage (Total Working Years, Age), and tenure at the organization are among the most significant factors influencing attrition.  The insight from this study is expected to help HR managers in developing effective, data-driven strategies to retain their talent in their organization.