Leaving missing values limited the number of applicable machine learning algorithms to XGBoost and LightGBM. Such approach to resolving missing data problem was outperformed by other applied techniques. Figure shows the results of F-score and balanced accuracy of handling missing data by filling with zeros and KNN imputation. It is visible that filling with zeros while marking which data was missing yielded better results. Although in some cases KNN imputation scored better, such as for KNN and SVC.