Group01 proposal

Proposal

Poster

Application

Research Paper

Abstract

Market Basket Analysis (MBA) is one of the key techniques used by large retailers to uncover associations between products brought by consumers. In our case, we apply a sequential version of MBA, called “sequential itemset mining” or “sequential pattern mining”, to analyze whether buying one item in the past indicates a higher likelihood of buying other things in the future. For instance, whether purchasing peanut butter implies sales of bread in the near future.

Market Basket Analysis

What is Market Basket Analysis?

Market basket analysis is a type of data mining technique to find association rules between different objects in a set, find frequent patterns in a transaction database, relational databases or any other information repository.

Application of Market Basket Analysis

This is often used in retail services to understand the products that are being purchased together. This would help the companies in designing their cross-sell or up-selling strategies. For example, assume product A has a relatively higher profit margin and it is often purchased together with product B. The company could bundle these two products to increase the sales of the products in order to increase its profit margin.

Key Concepts under Market Basket Analysis

There are a few key measurements under market basket analysis, which are:
- Support: Measure how frequent the item or item set appears in the transactions
- Confidence: Measure the likelihood that customers would buy the products shown in the rules, given that they have the products listed on the left hand side in their basket
- Lift: Co-occurence of products on the left hand side and right hand side exceeds the likelihood of products on left hand side and right hand side are independent

Following are the illustrations of the key concepts mentioned above:

Network Visualization Technique

Network Visualization is a technique often used to show the relationships between the different items. As the name suggested, this technique shows the relationship in network type of format, which is easier for the users to understand how different items are related to one another.

This visualization technique would complement the association rules mined from market basket analysis.

Case Study: Instacart Grocery Data

To illustrate how the network visualization technique can complement the association rules derived from market basket analysis, we will be using dataset from Instacart.

This data is downloaded from the The Instacart Online Grocery Shopping Dataset 2017 on Feb 2020. The data dictionary can be found this link.

About Data Set

Below is the summary of the dataset used:
- There are more than 33,000,000 transactions from about 206,000 unique customers
- The transactions contains the products purchased by the customers under each order
- The sequences of the transactions are also available in the dataset

Application UI

Rule network on categories or items of interest

Time series analysis of selected category or item (line chart or bar chart)

Show the most ranking rules over time & Display rule between two items or categories over time

Objects

1. Display the overview of relationship between category using Network diagram
2. Show product bundles that expect to be consumed simultaneously and key indicators of each
3. Interactively visualize how these bundles evolve over time
   

Packages Use

Package	Description
arules	Provides the infrastructure for representing, manipulating and analyzing transaction data and patterns (frequent itemsets and association rules).
arulesViz	Extends package 'arules' with various visualization techniques for association rules and item-sets. The package also includes several interactive visualizations for rule exploration.
tidyverse	The tidyverse is an opinionated collection of R packages designed for data science.
readr	Read Excel Files in R.
dplyr	Tools for Splitting, Applying and Combining Data.
ggplot2	Create graphics and charts.
Visnetwork	Create graphics and charts.

Storyboard

In this project, we adopted the following principles:
- Design thinking through having periodic check-in with Prof Kam to ensure we are heading the right directions
- Fail early through iterating the prototypes with Prof Kam to land the interface design of the ShinyApp

Prototype

After several rounds of discussions with Prof Kam and several iterations, below is the final prototype for the design apps:

Following are key highlights of designed ShinyApps as shown in the prototype:

Note that this final prototype has incorporated the feedbacks Prof Kam provided along the way.

Team Members

• Lok Jun Haur
• Wang Xingwen
• Xu Shifeng

References

• Market Basket Analysis using R.
• A Visual Application for Better Business Decision Making.
• A Gentle Introduction on Market Basket Analysis — Association Rules
• Tutorial: Sequential Pattern Mining in R for Business Recommendations