Procurement requirements and procurement analysis are approaches used by the Swedish National Agency for Public Procurement to ensure and maintain a sustainable societal development. The aim is to safeguard tax funds and ensure that they are used for their intended reason. In addition, the objective is to promote healthy competition between actors. One way to impose this is through supply chain management and spend analysis, which can help companies improve spend efficiency by gaining more insight into their supply chain. This thesis aims to explore the necessary prerequisites for Business Vision Consulting AB to develop and train a machine learning model used for classifying invoices to improve and facilitate spend analysis. By applying several preprocessing methods, two natural language processing algorithms and training predictive models using four different machine learning algorithms, this thesis proposes solutions to classify invoice lines to their corresponding UNSPSC-codes. The four chosen machine learning algorithms are: logistic regression, boosted decision trees, decision forest, and neural network. Out of the four proposed algorithms, logistic regression with n-gram features method to transform words to numbers, proved to be the most effective with classifying invoice lines. In the three highest levels, segment, family, and class, in the hierarchical structure of UNSPSC, a logistic regression model with n-gram features managed to produce an overall accuracy of 96.3%, 95.5%, and 99.0% respectively. While these accuracies are adequate, the end of the thesis proposes areas to delve deeper into for further improvements.