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Instacart Market Basket Analysis
- 1. INSTACART ASSOCIATION ANALYSIS PresentedBy, Sharanya Prathap Mount Carmel College B.VOC (Analytics) Batch 2018
- 2. Table of Content •Scope and objectives • Introduction • Modelling process Data extraction Data cleansing • Association analysis • Conclusion
- 3. Objective & Scope Objective •Our main objective was to analyze our data to Identify items based on the transaction history of customers. • Identify patterns of relationship between data of customers using association rules. Scope • Association Rule • Tools been used: R Studio, Microsoft Excel
- 4. What is Instacart? •Online grocery ordering app ,store. • Aims to Deliver Groceries in an Hour.
- 5. Modelling Process – DataExtraction Data is extracted from Kaggle. This is an anonymized data on customer orders over time.
- 6. - Data Cleaning Naturally,unstructured data. Hence, data cleaning (or cleansing, scrubbing) is important in further analysis. We cleaned our data, Orders data for days_since_prior_order consist of some missing values so first we will replace all our missing values with some mode of the values.
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- 8. EDA Objective 1 Identify theitems based on the transaction history of customers using affinity analysis.
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- 10. While most ofthe users have 8 products in their baskets, the average basket contains 10 products. For determining the number of products in the future baskets The idea is to look at the purchase history of each user, get the average number of items in the baskets and use this number for predicting the number of items in future baskets.
- 11. The count andlist the 15 most popular products in the basket
- 12. Fresh Veggie andFresh Fruits are most often sold by Aisle So, basically we conclude that Fruits,Veggies Products have high probability to be ordered by customers when he makes his next purchase
- 13. Milk or DairyProducts are the highest reordered by customer So, basically we conclude that Milk/Dairy Products have high probability to be ordered by customers when he makes his next purchase
- 14. Association Analysis: Association Identifieshow the data items are associated with each other. Association rules are created by analyzing data patterns and using the criteria support and confidence to identify the most important relationships.
- 15. Support and Confidence Support •Support measures the probability of collection of items being brought together. Confidence • Confidence measures that if a customer buys one product ‘A’ they will buy another product ‘B’, or A=>B. The confidence of A =>B can be estimated as frequency that someone will buy both A and B divided by the probability they will buy A.
- 16. Rule 1:Low supportand High Confidence Support=0.003269976 Confidence=0.01
- 17. Rule 1 Support=0.003269976 Confidence=0.01 rules <-apriori(transactions, parameter = list(supp = 0.003269976, conf = 0.01, maxlen=3), control = list(verbose = FALSE))
- 18. Rule 2:Support andConfidence Support=0.001 Confidence=0.4
- 19. Rule 2 Support=0.001 Confidence=0.4 rules2 <-apriori(transactions, parameter = list(supp = 0.001, conf = 0.4, maxlen=3), control = list(verbose = FALSE))
- 20. Rule 3 :High Confidence and less support Support=0.005 Confidence=0.1
- 21. Rule 3 Support=0.005 Confidence=0.1 rules3 <-apriori(transactions, parameter = list(supp = 0.005, conf = 0.1, maxlen=3), control = list(verbose = FALSE))
- 22. Conclusion Using the associationrules (rule 1-3), the next purchase of a customer can be predicted based on his purchase history. Rules can be refined further based on support and confidence combination. Using Jakart Index affinity between different item combinations can be calculated which would help in prediction of next purchase of customer.
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