The accurate prediction of the price of products can be highlybeneficial for the procurers both businesses wised and productionwise. Many companies today, in various fields ofoperations and sizes, have access to a vast amount of datathat valuable information can be extracted from them. In thismaster thesis, some large databases of products in differentcategories have been analyzed. Because of confidentiality, thelabels from the database that are in this thesis are subtitled bysome general titles and the real titles are not mentioned. Also,the company is not referred to by name, but the whole job iscarried out on the real data set of products. As a real-worlddata set, the data was messy and full of nulls and missing data.So, the data wrangling took some more time. The approachesthat were used for the model were Regression methods andGradient Boosting models.The main purpose of this master thesis was to build priceprediction models based on the features of each item to assistwith the initial positioning of the product and its initial price.The best result that was achieved during this master thesiswas from XGBoost machine learning model with about 96%accuracy which can be beneficial for the producer to acceleratetheir pricing strategies.