Predicting the size of a company winning a procurement: an evaluation study of three classification models

Sammanfattning: In this thesis, the performance of the classification methods Linear Discriminant Analysis (LDA), Random Forests (RF), and Support Vector Machines (SVM) are compared using procurement data to predict what size company will win a procurement. This is useful information for companies, since bidding on a procurement takes time and resources, which they can save if they know their chances of winning are low. The data used in the models are collected from OpenTender and allabolag.se and represent procurements that were awarded to companies in 2020. A total of 8 models are created, two versions of the LDA model, two versions of the RF model, and four versions of the SVM model, where some models are more complex than others. All models are evaluated on overall performance using hit rate, Huberty’s I Index, mean average error, and Area Under the Curve. The most complex SVM model performed the best across all evaluation measurements, whereas the less complex LDA model performed overall worst. Hit rates and mean average errors are also calculated within each class, and the complex SVM models performed best on all company sizes, except the small companies which were best predicted by the less complex Random Forest model.