A Product Network Analysis Using A Priori Algorithm for Extending the Market Basket in Retail

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.