Parkinson's disease is the second most common neurodegenerative disorder and affects more than 7 million people globally. In this work, we classify subjects with Parkinson's disease using data from finger-tapping on a keyboard. We use a free database by Physionet with more than 9 million records, preprocessed to delete atypical data. In the feature extraction stage, we obtained 48 features. We use Google Colaboratory to train, validate, and test nine supervised learning algorithms that detect the disease. As a result, we achieve a degree of accuracy higher than 98%.