Data mining in market basket analysis

Enterprise Systems Engineering Project
Data Mining in Market Basket Analysis
Tanmayee Mandala, Lubna Tarawneh, Nikhil Mohkhedkar &
Prathamesh Kulkarni
State University of New York at Binghamton
December 4th, 2019

2Binghamton University | December, 2019
Agenda
 Introduction
 Problem Description
 Objectives
 Data Collection
 Methods
 Results
 Decision Making
 Implementing DSS
 Conclusion

Market Basket Analysis
 Fundamental technique used by retailers to uncover association between items
 Used to analyze the customer purchasing behavior and helps in increasing the
sales and maintaining inventory by focusing on point of sale transactional data
 Provides insight into which products tend to be purchased together and which
are most amenable to promotion

Data Mining in Market Basket Analysis
 Process of extracting patterns (knowledge) from data
 Helps in predicting future trends
 Association rules: if a customer buys product 1 then it is expected that he
buys product 2
 Trivial rules: people who buy chalk-piece also buy duster
 Inexplicable rules: people who buy mobile also buy bag
 Actionable rules: must satisfy a minimum support and
confidence threshold to be considered

Problem Description
 Large number of people visit retail stores each day
 Huge amount of data is collected and stored in large transactional database
 Transactions can be used to help managers make important decisions to
increase their sales figures
 Cross selling of items
 Proper placement of frequently purchased and EDLP items
 Change store layout

Objectives
 Understand customer behavior
 Gain insights of large transactional data by applying data mining techniques
 Find frequently purchased item sets and patterns from transactions
 Proper placement of frequent and EDLP items
 Dynamic visualization of results
 Support the manager by making an effective DSS to help with decision-
making process

Data Collection
 Online data for small retail store
 One week of data
 3 data sets collected (7,000+ customers orders)
 60 different products bought
Basket Product 1 Product 2 Product 3 Product 4
1 Frozen Vegetables Spaghetti Green Tea -
2 Burgers French Fries Low Fat Yogurt Green Tea
3 Asparagus Salad - -
4 Ground Beef Pepper Mineral Water Chocolate
5 Butter Light Mayo Fresh Bread -
6 Pancakes Eggs - -
7 Frozen Vegetables Chicken Pancakes -

Assumptions
 Only grocery section of a small retail store is considered when implementing
DSS
 Different product sections not considered when placing the products in aisles
 Sales data not available and is randomly generated
 Results based on small set of data, when using bigger set of data the results
would be more accurate and can give better insights

Methodology
Apriori Algorithm: most established algorithm for finding frequent item sets
Scan the transaction data base to get the support
of S each 1-itemset, compare S with min_sup,
and get a support of 1-itemsets, L1
Use L(k-1) join L(k-1) to generate a set of
candidate k-itemsets. And use Apriori property to
prune the unfrequented k-itemsets from this set.
STEP 1
STEP 2
For every nonempty subset s of 1, output the rule
“s=> (1-s)” if confidence C of the rule “s =>
(1-s)” (=support s of 1/support S of s)’min_conf
STEP 6
Scan the transaction database to get the support
S of each candidate k-itemset in the find set,
compare S with min_sup, and get a set of
frequent k – itemsets L(k)
STEP 3
The candidate
set = Null
STEP 4
For each frequent itemset 1, generate all
nonempty subsets of 1
STEP 5
NO
YES

Results of Apriori Algorithm
 Association rules obtained
 Bigger set of data can give more accurate rules
 Knowledge is extracted
 Rules obtained:
 ['pasta']  ['Almonds']
 ['Sugar’]  ['Coffee']
 ['mushroom cream sauce’]  ['escalope']
 ['herb & pepper'] ['noodles']
 ['tomato sauce']  ['noodles']
 ['whole wheat pasta'] ['olive oil']
Sugar
Coffee

Decision Making
 Put the products that are purchased together close to one another
 Help customers to notice them
 Increase the revenues of these products
 Improve the customers shopping experience
 Put EDLP items near frequently purchased items
 EDLP items account for high percentage of sales
 Making them more visible to customers can increase their sales
 Identify the most selling item to decide the location of advertisement

Implementing DSS: Dynamic Aisle Demo in Python

Dashboard Using Power BI (1/3)

Conclusion and Future Work
 In addition to its popularity as a retailers technique Market Basket Analysis
can be applicable in many other areas:
 Manufacturing industry for predictive analysis of equipment failure
 Pharmaceutical for the discovery of co-occurrence relationships among diagnosis
and pharmaceutical active ingredients prescribed to different patient groups
 Banking for fraud detection based on credit card usage data
 Analyzing customer behavior by associating purchases with demographic and
socio-economic data
 More and more organizations are discovering ways of using market basket
analysis to gain useful insights into associations and hidden relationships

Data mining in market basket analysis

More Related Content

What's hot

Similar to Data mining in market basket analysis

Recently uploaded

Data mining in market basket analysis