The crucial factor for every successful business is the understanding of it’s customers. To increase sales and profit companies have to discover what their customers like, how much money are they able to spend on offered items and which item categories are they willing to buy together in a single transaction. My topic is exploratory data analysis on bakery transactions. The main aim of the analysis is to find the hidden information or pattern in the bakery data and to figure out what items people tend to buy together. The analysis is carried out using the Apriori algorithm for frequent itemset mining, and then using the results of the Apriori algorithm to generate association rules to find interesting relationships in our data.
The data for the analysis is obtained from the Kaggle website. The data is spread across files "BreadBasket_DMS.csv”. The file contains the information about the transcations people have performed at a bakery. The bakery dataset contains 21293 rows and 4 columns which includes ‘Date’, ‘Time’, ‘Transaction’ and ‘Item’ that was bought.