Products Frequently Bought Together in Stores Using classification models
- 1. Products Frequently Bought Together in Stores Using classification models Supervised by Dr. Amina dahim 2023 - 2024 Submitted by M.Sc. Students Sabreen Salih Mahdi Zahraa Fouad Rasool Hiba Tullah Ziyad
- 2. Introduction . Motivation of frequent item sets in online stores . Association rule . Association rule Advantages/ Disadvantages . A-priori Algorithm for computing frequent item sets . A-priori Algorithm Advantages/ Disadvantages . What Applications use A-priori Algorithm . Conclusion . Outlines
- 3. Introduction Frequently bought together purchase recommendations are one of the most impactful stores strategies that have the potential to maximize the return on investment on in-house product stores. Frequently bought together are the products that your customers usually purchase with specific items in your store. Displaying the right products at the right time can help drive the discovery and sales of your products.
- 4. One of the most important strategies for influencing sales is the offering of discounts. A discount would enable the salesperson to increase the average order value of the transactions. Smart upselling and cross-selling technique that fits into natural buying habit. Introduction
- 5. Motivations behind frequent sets of products in online stores An easy way to offer shopping assistance. The frequently bought together section act as a shopping assistant by suggesting the best-suited complementary products. Manually curating frequently bought together products lets you maximize revenue and profits for your store
- 6. products as frequently bought together would help you remind your customers of the products that they might need. Products that bought together helps you boost your sales and profits. Motivations behind frequent sets of products in online stores
- 7. This concept itself is derived from the terminology of market basket analysis, namely the search for relationships of several products in a purchase transaction. Most machine learning algorithms work with numeric datasets and hence tend to be mathematical. However, association rule mining is suitable for non-numeric, categorical data. Understanding consumer buying behavior is compulsory in business Association rule Mining
- 8. Given a set of transactions, each of which is a set of items, find all rules (XY) that satisfy user specified minimum support and confidence constraints. Support = (#T containing X and Y)/(#T) Confidence = (#T containing X and Y)/ (#T containing X) Applications Cross selling and up selling Supermarket shelf management Association rule Mining
- 9. What does association analysis do? Example
- 10. Example
- 11. Example
- 12. Example
- 13. Simple Example
- 14. Steps
- 15. Itemset
- 17. Support
- 18. Confidence
- 19. Advantages The employed algorithms have too many parameters for someone who is not a data mining expert. The disadvantage of association algorithms is that they are trying to find patterns within a potentially very large search space and, hence, can require much more time to run than a decision tree algorithm. Association rule Advantages/ Disadvantages
- 20. A priori Algorithm : is an significant algorithm for mining frequent item sets for Boolean association rules. It contains two processes:- Detect all frequent itemsets by scanning DB. Form strong association rules in the frequent itemsets. A priori pruning principle: If there is any itemset which is infrequent, its superset should not be generated/tested! A- priori Algorithm for computing frequent itemsets
- 21. Method: Initially, scan DB once to get frequent 1- itemset Generate length (k+1) candidate itemsets from length k frequent itemsets Test the candidates against DB Terminate when no frequent or candidate set can be generated A- priori Algorithm for computing frequent itemsets
- 22. Transaction ID Items T1 Hot Dogs , Buns , ketchup T2 Hot Dogs , Buns T3 Hot Dogs , Coke , Chips T4 Chips , Coke T5 Chips , ketchup T6 Hot Dogs , Coke , Chips Find the frequent itemsets on this table, assume that minimum support count = 3 TDB Itemset Sup- count Hot Dogs 4 Buns 2 ketchup 2 Coke 3 Chips 4 C1 1ST SCAN Itemset Sup- count Hot Dogs 4 Coke 3 Chips 4 Compare candidate support count with minimum support count L1
- 23. Itemset Sup- count Hot Dogs , Coke 2 Hot Dogs , Chips 2 Coke , Chips 3 Find the frequent itemsets on this table, assume that minimum support count = 3 Itemset Hot Dogs , Coke Hot Dogs , Chips Coke , Chips Itemset Sup- count Coke , Chips 3 C2 2nd scan L2 Compare candidate support count with minimum support count
- 24. Advantages - Uses large itemset property - Easily parallelized - Easy to implement Disadvantages - Assumes transaction database is memory resident - Requires many database scans A- priori Algorithm Advantages/ Disadvantages
- 25. Education. Medicine. Biology. E-commerce & Recommendation. What Applications use this Algorithm ?
- 26. Market Based Analysis is one of the key techniques used by large relations to show associations between items. it can generate association rules from the given transactional datasets. Association rules are useful for analyzing and predicting customer behavior. The disadvantage of association algorithms is require much more time to run than a decision tree algorithm. The A priori Algorithm is an instrumental algorithm for mining familiar item sets. The disadvantage is more exploration space and computational cost is too expensive. Conclusion
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