Bakery-Transaction-Analysis

Authors: Shuoqi Zhang, Trista Lin.

Objectives:

• To analyze the association rules of items purchased and to define actions to boost the sales.
• To predict the number of transactions at a given time in hour and to provide insights about how external variables impact the sales.
• To identify and explain any possible segment of transactions.

Data Source:

• CSV dataset of transaction records of a bakery located in the old town of Edinburgh, UK (https://www.kaggle.com/sulmansarwar/transactions-from-a-bakery)
• Weather-related data were extracted from Meteostat (a Python library, https://dev.meteostat.net/python/hourly.html#example)

Data Cleaning:

• The cleaned dataset was splitted into weekday and weekend datasets due to different patterns observed. Results from weekend data are illustrated as examples below.

Data Analysis I: Market Basket Analysis

Data Analysis II: Random Forest

Data Analysis III: K-means Clustering