In business, employee attrition is when employees leave the company for whatever reason, either they've found a new job or retired, and haven't been replaced immediately. For a company to be successful, it needs not only to attract top talent but it also needs to retain these talents. For this reason, in this project we'll look into a dataset containing information regarding a certain company's employee list to try to find patterns that may provide useful information in understanding why employees leave and to predict the employee attrition.

This project involved the use of machine learning for predicting employee attrition, with a focus on implementing various preprocessing techniques such as categorical encoding and feature scaling, as well as using SMOTE to handle class imbalance. Model selection and hyperparameter tuning were also carried out, resulting in a support vector machine model with an F1 score of 98%.