The Presentation is regarding the Market Basket Analysis Concept which is done practically with the real world data from a small Canteen. This is completely a real time data on which the analysis results are drawn.

1. MARKET BASKET ANALYSIS

2. MARKET BASKET
ANALYSIS
BY
PRAVITA RAJA
SAYAN BOSE
SIDDHARTHA PANAPAKAM
TULIKA JEENDGAR
MARKET BASKET ANALYSIS

3. DEFINITION & INTRODUCTION
Market Basket Analysis (MBA) is a data mining technique which is
widely used in the consumer package goods (CPG) industry to
identify which items are purchased together and, more importantly,
how the purchase of one item affects the likelihood of another item
being purchased.
Also called Data Mining Technique
How many people brought Product A
People who purchased Product A also
generally buy which other products
Students who enrolled in course A also
frequently enroll in which other courses etc.
MARKET BASKET ANALYSIS

4. MBA in Retail Scenario
Each customer purchases different set of products, different quantities,
different times
MBA uses this information to-
Customer Identification
Understand why they make certain purchases
Gain Insights about its products(Fast & Slow movers)
Products which are purchased together
Products which might benefit from promotion
Take Action:
Store Layouts
Which products to put on specials, promote, coupons
MARKET BASKET ANALYSIS

5. FACTS MBA
ADVANCED or
ZOOM_ABLE
MBA
BASIC MBA
 Market basket data is one of the actionable potentials enabled
by collaborative POS data management
 Can answer lots of interesting and important business questions
MARKET BASKET ANALYSIS

6. Apriori algorithm
MARKET BASKET ANALYSIS
Apriori an algorithm for frequent item set mining and association
rule learning over transactional databases..
It proceeds by identifying the frequent individual items in the
database and extending them to larger and larger item sets as long
as those item sets appear sufficiently often in the database.
The frequent item sets determined by Apriori can be used to
determine association rules which highlight general trends in
the database.

7. MARKET BASKET ANALYSIS
Apriori Algorithm

8. MARKET BASKET ANALYSIS
ASSOCIATION RULE
Association rules are if/then statements that help uncover
relationships between seemingly unrelated data in a
transactional database, relational database or other information
repository.

9. MARKET BASKET ANALYSIS

10. MARKET BASKET ANALYSIS
RECOMMENDATIONS HEALTHCARE
SEQUENCE
DISCOVERY
FRAUD DETECTION
APPLICATION OF
ASSOCIATION RULES

11. Analysis of MBA on anna’s
shop
MARKET BASKET ANALYSIS

12. MARKET BASKET ANALYSIS
Item-sets No. of Occurrence
Samosa, Smoke 8
Tea, Smoke 45
Tea, Biscuit 12
Tea, Vadapav 40
Tea, Samosapav 20
Smoke, Mint 15
Maggie, Egg 12
Bournvita Milk, Biscuit 8
Tea, Vadapav, Smoke 25
Maggie, Smoke 8
Tea, Poha 5
Eggpav, Tea 10
Maggie, Tea, Vadapav 4
TOTAL 212
The daily sales data of Anna’s shop

13. MARKET BASKET ANALYSIS
Occurrence of Each Item Individually
Item-sets Support Count
Tea 168
Smoke 83
Biscuit 20
Samosapav 20
Mint 15
Egg 12
Bournvita Milk 8
Maggie 31
Eggpav 10
Vadapav 69
Samosapav 8
Minimum Support
Count = 25
Support Count – Frequency of occurrence of an item-set.

14. MARKET BASKET ANALYSIS
Occurrence of combination of 2 items
Item-sets Support Count
Tea, Biscuit 12
Tea, Smoke 72
Tea, Vadapav 69
Tea, Samosapav 20
Samosa, Smoke 8
Smoke, Mint 15
Maggie, Egg 12
BournvitaMilk, Biscuit 8
Maggie, Smoke 15
Tea, Poha 5
Eggpav, Tea 10
Maggie, Tea 11
Maggie, Vadapav 4
Minimum Support
Count = 25

15. MARKET BASKET ANALYSIS
Occurrence of Combination of 3 Items
Item-sets Support Count
Tea, Vadapav, Smoke 25
Tea, Vadapav, Maggie 4
Minimum Support Count = 25
Support Count – Frequency of occurrence of an item-set.

16. MARKET BASKET ANALYSIS
Item-set Support Count Support
Tea, Smoke 72 0.34
Tea, Vadapav 69 0.33
Tea, Vadapav, Smoke 25 0.12
Tea, Vadapav, Maggie 4 0.02
Support- fraction of transaction that contain an item-set.
Support = support count
total no. of occurrence
Minimum Support = 0.10

17. MARKET BASKET ANALYSIS
Item-set Support (%) Confidence Lift
Tea, Smoke 39 43 0.84
Tea, Vadapav 32 41 1.28
Tea, Vadapav,
Smoke
39 36 0.92
Smoke, Tea 79 88 1.11
Vadapav, Tea 79 100 1.26
Tea, Smoke,
Vadapav
32 35 1.09
Tea, Vadapav,
Maggie 14 6
0.42
Confidence = support count
support count of first variable
Lift = Confidence
Support of the second variable
Minimum
Confidence = 0.10
Confidence- estimation of conditioned probability.
Lift- measure of confidence of combination as per the support of one item..

18. MARKET BASKET ANALYSIS
Conclusion of this analysis:
• People who are going for smoke or vadapav will obviously have tea as
the lift for smoke & tea or vadapav & tea is more than one.
• Lift (tea, smoke, vadapav) = 1.09 > Lift (tea, vadapav, smoke) indicates
that tea and smoke have a greater impact on the frequency of vadapav
than tea and vadapav have on smoke.
• This shows that Anna should cross-sell Vadapav with tea and smoke.

19. MARKET BASKET ANALYSIS
Cross selling: AMAZON.COM
Example: People who read “HISTORY of PORTUGAL” were
also interested in “NAVAL HISTORY”.
Dividing customers: On the basis of purchasing same item for different
reasons.
Example: purchasing eggs for making cake with flour
and sugar whereas for making omelets with cheese
ETC.
Beer and Diapers.

20. MARKET BASKET ANALYSIS

21. MARKET BASKET ANALYSIS
ADVANTAGES
OF MARKET
BASKET
ANALYSIS

22. MARKET BASKET ANALYSIS
PRIVACY ISSUES
SECURITY ISSUES
MISUSE OF INFORMATION / INACCURATE
INFORMATION