This paper describes the creation of a database and a machine learning model to predict employee attrition. Our proposal deals with attrition by considering 3 classes (voluntary, involuntary and no attritors) giving a more complete view of the loss of qualified personnel to the Human Resources Management. Of the several machine learning models tested to solve the problem, XGBoost stood out as the best performing one on a dataset with more than four thousand employees and twenty-one features collected from three independent companies from different industrial sectors. The model, evaluated on a 20-run experiment, achieved an overall mean accuracy of 78.5%, corresponding to the correct classification of 52.6% of the voluntary attritors, 78.9% of the involuntary attritors and 81.6% of the non-attritors, showing that voluntary attritors are harder to discriminate.