An effort to study the ordering pattern of customers in a fast food outlet using FP growth algorithm

The task of finding frequent pattern in large databases is very essential and has been studied in large scale for the expansion of any product in a Company. This assignment is computationally costly when countless exist. Visit designs are designs that show up as often as possible in a dataset. The disclosure of intriguing connection relationship among gigantic measures of business exchange records can help in numerous business basic leadership process. The FP growth algorithm can be used to find frequent item sets without using candidate generations. The successive example tree is utilized to store packed and focal data about regular examples that can be utilized to mine continuous examples in extensive databases. This paper talks about the FP Tree idea and tries to examine the requesting example of clients in a fast food outlet. This approach decides the affiliation decides that happen in the dataset of a fast food outlet that can be utilized to outline business procedures to upsurge income for a specific outlet when the deal goes low. Being given a set of transactions of the customers the purpose of the association rule is to find correlations between the sold products so that the decisions can be made in terms of offering the combo offers or packages to customers to implement successful marketing techniques for an outlet. It is interesting to learn the association between the customer's order when we find that every customer orders a pizza or a burger along with a soft drink like Pepsi or a starter like French fries. The knowledge of frequent pattern increases the sale of the soft drinks or any snack that is ordered along with it. This pattern helps the Company to improve their sales and increase their revenue. The implementation of FP growth algorithm will be to a great degree helpful in advertise inquires about. We can discover concealed data and relationship from the information and further choices can be taken in view of the gained data.