Survey, Analysis and Association Rules derivation using Apriori Method for buying preference amongst kids of age-group 5 to 9 in India

Abstract

what used to be an annual or bi-annual phenomenon of toys/sports/utility item purchase nearly two decades back, is a weekly/bi-weekly/daily transaction now-adays. Toys purchase by kids in a very frequent transaction that happens almost every alternate day in a big segment of society and thus produce high volumes of data. Consequently, there is rising scope to apply data mining methods to obtain toys/items_of_interest buying patterns amongst kids. In the present piece of research, we have applied Apriori algorithm to perform data mining using the data collected through a Google form after circulating children’s (age group 5-9) acquaintance of toys, across the country; the survey got carried out through students of two engineering colleges where diverse group of students from different parts of the country are studying. Nine association rules were achieved after applying Apriori Algorithm on the data set of the Toys/Sports items thus formed. Further, accuracy of framed rules has also been manually validated by the store owner; Beyblades and Carom are the most preferred toys/sports items; whereas Bicycle and Bat-Ball falls at second position in the list. The results provide very useful association amongst toys/Sports.