Market basket analysis provides an insight into customer consumption patterns and trends in the industry. These will be achieved by analyzing and studying the performance of the large datasets of transactions made by consumers held in retail stores. These commercial transactions will be analyzed using the Machine Learning technique called the A priori algorithm by establishing association rules and determining those groups of items in a market basket whose association could represent better economic benefits for companies. This study will analyze the historical sales data of the product groups, in order to identify relationships that al-low companies in the sector to generate patterns to propose the increase of their portfolio based on the products with the greatest purchasing trends. At the end of this investigation, commercial strategies will be proposed to improve sales, take advantage of spaces in stores and implement more effective strategic offers, based on the groups of articles with the best associations found.