BackgroundMachine learning is an area that is being explored with great haste these days, which inspired this study to investigate how seven different supervised learning algorithms perform compared to each other. These algorithms were used to perform classification tasks on logistics consignments, the classification is binary and a consignment can either be classified as missed or not.

ObjectivesThe goal was to find which of these algorithms perform well when used for this classification task and to see how the results varied with different sized datasets. Importance of the features which were included in the datasets has been analyzed with the intention of finding if there is any connection between human errors and these missed consignments.

MethodsThe process from raw data to a predicted classification has many steps including data gathering, data preparation, feature investigation and more. Through cross-validation, the algorithms were all trained and tested upon the same datasets and then evaluated based on the metrics recall and accuracy.

ResultsThe scores on both metrics increase with the size of the datasets, and when comparing the seven algorithms, two does not perform equally compared to the other five, which all perform moderately the same.

Conclusions Any of the five algorithms mentioned prior can be chosen for this type of classification, or to further study based on other measurements, and there is an indication that human errors could play a part on whether a consignment gets classified as missed or not.