In this project we got familiar with one of the most important algorithms in data science field which is “Apriori Algorithm”, this algorithm is given by R. Agrawal and R. Srikant in 1994 for finding frequent item sets in a dataset for Boolean association rule. Name of the algorithm is Apriori because it uses prior knowledge of frequent itemset properties. We apply an iterative approach or level-wise search where k-frequent item sets are used to find k+1 item sets. By applying this algorithm we are able to understand the different relation between the data that we acquired from different approaches and that will help us get more close look on how the data are handled and how can we increase the efficiency of the work and provide more useful offers to the clients by understanding the way they think and deal with the different offers and materials. Learning and understanding all these data and the rules between them and the different relations that contains tons of data we would be able to apply different approaches and algorithms in different fields to increase efficiency in different fields and increase the production process with tons of useful data and new methods of development. In this project we managed to deal with the data set and analyze and calculate the support, confidence, lift and the leverage which provides a lot of data and information about our data set which will help us understand more how to work with them and provide new method to deal with these data.
first_frequent_itemset:This function determine the first itemsets from the attributes which have a support higher than the minimum support. levels_combinations support combinations_rules confidence