Market Basket Analysis

The video tutorial explains the implementation of the Apriori algorithm for market basket analysis. It covers creating a dataset, preprocessing data using transaction encoding, and applying the Apriori algorithm to find frequent itemsets. The tutorial also discusses generating and analyzing association rules to gain insights into product placement and cross-selling opportunities.

What is the primary goal of using the Apriori algorithm in market basket analysis?

• To reduce the cost of items

• To improve customer service

• To identify frequent item combinations

• To increase the number of transactions

What is the first step in preparing data for the Apriori algorithm?

• Generating association rules

• Data preprocessing

• Calculating lift

• Setting minimum confidence

Which module is used to import the Apriori and association rules functions?

• Pandas

• NumPy

• MLxtend

• SciPy

What does a minimum support of 30% indicate in the Apriori algorithm?

• Items must appear in at least 30% of transactions to be considered frequent

• Items must be purchased together 30% of the time

• The algorithm will only consider 30% of the data

• The confidence level is set to 30%

What does a high lift value indicate in association rules?

• Items are less likely to be bought together

• Items are more likely to be bought together

• The rule is not significant

• The rule has low confidence

Which items were found to be highly correlated in the analysis?

• Cookies and milk

• Bread and butter

• Eggs and butter

• Milk and eggs

What unexpected association was discovered in the analysis?

• Cookies and milk

• Cookies and bread

• Eggs and butter

• Milk and bread