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Market Basket Analysis for Bakery
Retail & Consumer Behavior
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About
This dataset is designed for Market Basket Analysis, a key technique used by large retailers to increase sales by understanding customer purchasing behaviour and patterns [1]. It examines collections of items to find relationships between products frequently purchased together within a business context [1]. The data specifically pertains to online transaction details from "The Bread Basket", a bakery located in Edinburgh, collected between 30th October 2016 and 9th April 2017 [2]. It is ideal for those looking to practice association rule mining and gain insights into the business applications of data mining for understanding customer buying patterns [3].
Columns
- TransactionNo: A unique identifier assigned to every single customer transaction [2, 3].
- Items: The specific items purchased by customers in a given transaction [2, 4].
- DateTime: The date and time stamp recording when each transaction occurred [2, 4].
- Daypart: Categorises the part of the day a transaction took place, such as morning, afternoon, evening, or night [2, 5].
- DayType: Classifies whether a transaction happened on a weekday or during the weekend [3, 6].
Distribution
The dataset is in a CSV format and comprises 20,507 entries across over 9,000 unique transactions [2]. It contains 4 columns [2].
All entries for
TransactionNo, Items, DateTime, Daypart, and DayType are marked as 100% valid, with no mismatched or missing values [4-7]. The file size is 1.04 MB [3].- Items: Coffee is the most common item, making up 27% of purchases, followed by Bread at 16%. The remaining 57% are grouped as 'Other', indicating a wide variety of less frequently purchased items [4]. There are 94 unique items in total [4].
- Daypart: The majority of transactions occur in the Afternoon (56%), followed by the Morning (41%) [6]. A small percentage (3%) falls into an 'Other' category, likely representing Evening and Night transactions [6].
- DayType: Weekdays account for 62% of transactions, with weekends making up 38% [6].
Usage
This dataset is well-suited for a variety of analytical tasks:
- Performing Market Basket Analysis to discover item associations [1, 3].
- Implementing association rule mining algorithms to identify purchasing patterns [1, 3].
- Understanding and predicting customer purchasing behaviour [1].
- Gaining business insights from sales data to optimise marketing strategies and product placement [1, 3].
- Exploring temporal patterns in sales, such as preferred shopping times (daypart, daytype) [2, 3].
Coverage
The data originates from "The Bread Basket" bakery in Edinburgh [2]. It covers online transactions spanning from 30th October 2016 to 9th April 2017 [2].
- Temporal Scope: While the main transaction period is between late 2016 and early 2017, the earliest recorded transaction date in the dataset is 11th January 2016, and the latest is 3rd December 2017 [5].
- Customer Behaviour: Insights into purchasing patterns across different times of the day (morning, afternoon, evening, night) and week (weekdays, weekends) are available [2, 3, 6].
- Product Assortment: Details of a wide range of bakery items purchased, with Coffee and Bread being the most popular [4].
License
CC0: Public Domain
Who Can Use It
- Retailers and e-commerce businesses looking to understand customer behaviour and increase sales [1].
- Data scientists and analysts who wish to practice and apply association rule mining techniques [3].
- Marketing professionals seeking to develop targeted campaigns based on purchasing patterns [3].
- Business intelligence specialists interested in optimising sales and inventory management [1].
Dataset Name Suggestions
- Bakery Online Sales Transactions
- Edinburgh Bread Basket Sales Data
- Market Basket Analysis for Bakery
- Retail Transaction Patterns (Bakery)
Attributes
Original Data Source: Market Basket Analysis for Bakery
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