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The Market Basket Analysis (MBA) method of data mining looks for a collection of items that frequently occur together in a large dataset or database. This technology is used in various industries like retail to promote cross selling and to help in product placement and in fraud detection and other uses. Understanding consumer purchasing trends and preferences has been made easier with the use of this technology. The use of it in international corporations is widespread. Along with the advancement of technology, current business trends have undergone a significant transformation. This technique analyses the purchasing habits of customers by determining the relationships that exist between the various items that are placed in the customers' shopping baskets. Because of shifts in the expectations of customers, it is essential for businesses to improve the precision of their operations. In this particular scenario, we examine a neighbourhood grocery store to investigate and contrast the Apriori and FP Growth algorithms' respective run times. In this paper we also find the combination of products that are sold together.