This study explores the application of a recommendation engine in collaboration with Fortnox. The primary focus of this paper is to find potential improvements for their recommendation engine in terms of accurate recommendation for users. This study evaluates the performance of various algorithms on imbalanced data without resampling, using EasyEnsemble undersampling, SMOTE oversampling, and weightedclass approaches. The results indicate that LinearSVC is the best algorithm without resampling. Decision Tree performs well when combined with EasyEnsemble, outperforming other algorithms. When using SMOTE, Decision Tree performs thebest with the default sampling strategy, while LinearSVC and MultinomialNB show similar results. Varying the threshold for SMOTE produces mixed results, with LinearSVC and MultinomialNB showing sensitivity to changes in the threshold value,while Decision Tree maintains consistent performance. Finally, when using weightedclass, Decision Tree outperforms LinearSVC in terms of accuracy and F1-Score.Overall, the findings provide insights into the performance of different algorithmson imbalanced data and highlight the effectiveness of certain techniques in addressing the class imbalance problem, and the algorithms’ sensitivity to changes with resampled data.