1. What is the main goal of Market Basket Analysis (MBA)?
A. To predict future stock prices
B. To identify associations or relationships between products bought together
C. To classify customers into different segments
D. To optimize website content
Answer: B
(The main goal of MBA is to identify relationships between products in a transaction dataset, such as products that are frequently bought together.)
2. Which of the following measures is used to evaluate the strength of an association rule in Market Basket Analysis?
A. Accuracy
B. Support
C. Recall
D. Precision
Answer: B
(Support is a measure that helps evaluate the frequency of an itemset in the dataset and is crucial in Market Basket Analysis.)
3. In Market Basket Analysis, what does support represent?
A. The proportion of transactions that contain a specific product
B. The ratio of correct predictions
C. The probability that two items are bought together
D. The likelihood that a product will be purchased in the future
Answer: A
(Support refers to the proportion of transactions that include a particular item or itemset.)
4. What does confidence in Market Basket Analysis measure?
A. The likelihood that a product will be purchased in the future
B. The strength of the rule’s relationship between items
C. The proportion of transactions that include a specific item
D. The probability that an itemset occurs together in a transaction
Answer: B
(Confidence measures the probability that if one item is purchased, the other item will also be purchased.)
5. What is the lift in Market Basket Analysis used to measure?
A. The likelihood that an item will be purchased
B. The strength of association between items, adjusted for their individual frequencies
C. The total number of items sold
D. The time spent by customers in a store
Answer: B
(Lift measures how much more likely two items are to be purchased together compared to if they were bought independently.)
6. Which of the following algorithms is commonly used for performing Market Basket Analysis?
A. K-means clustering
B. Apriori algorithm
C. Support Vector Machine (SVM)
D. Naive Bayes classifier
Answer: B
(The Apriori algorithm is widely used for Market Basket Analysis to find frequent itemsets and generate association rules.)
7. In Market Basket Analysis, if the confidence of an association rule is 1.0, what does that imply?
A. The rule is useless
B. The two items are always purchased together
C. The items are rarely purchased together
D. The rule is statistically insignificant
Answer: B
(A confidence of 1.0 means that if one item is purchased, the other item is always purchased with it.)
8. In the context of Market Basket Analysis, which of the following is a frequent itemset?
A. An item that is purchased infrequently
B. A collection of items that appears in a sufficient number of transactions
C. An item that is never purchased
D. A product that is discounted
Answer: B
(A frequent itemset is a combination of products that appear in a large number of transactions, based on a minimum support threshold.)
9. What is the main challenge in Market Basket Analysis?
A. The inability to predict the future
B. The computational complexity due to large datasets
C. Determining which products should be discounted
D. Analyzing only structured data
Answer: B
(The main challenge in MBA is dealing with the computational complexity of analyzing large datasets and generating association rules for frequent itemsets.)
10. In Market Basket Analysis, what is meant by association rule mining?
A. Finding items that are always sold together
B. Identifying frequently occurring patterns and relationships between items in transaction data
C. Grouping customers into different categories
D. Predicting the prices of items
Answer: B
(Association rule mining is the process of discovering frequently occurring patterns or relationships between items in a dataset, typically applied in Market Basket Analysis.)
11. Which of the following is NOT a typical application of Market Basket Analysis?
A. Recommending products to customers based on previous purchases
B. Identifying fraud in financial transactions
C. Designing store layouts based on frequent itemsets
D. Optimizing cross-selling and upselling strategies in e-commerce
Answer: B
(While Market Basket Analysis can be used to recommend products, identify frequent itemsets, and optimize retail strategies, it is not typically used for fraud detection.)
12. Which of the following is TRUE about association rules in Market Basket Analysis?
A. All association rules are useful for predicting future purchases
B. Association rules do not require any threshold for support or confidence
C. Association rules provide insights into how items are related in transactions
D. Association rules are always deterministic
Answer: C
(Association rules provide insights into how items are related and purchased together in transactions, with the use of support, confidence, and lift to measure the strength of these relationships.)
**13. In Market Basket Analysis, which of the following is the correct interpretation of the association rule:
{Bread} → {Butter} (Confidence: 0.8, Support: 0.1, Lift: 1.5)?
A. 80% of the time, when bread is purchased, butter is also purchased
B. 10% of all transactions include both bread and butter
C. Bread and butter are purchased together 80% of the time
D. There is no significant relationship between bread and butter
Answer: A
(The rule indicates that 80% of the time, when bread is purchased, butter is also purchased, as per the confidence value.)
14. Which of the following could be a real-world application of Market Basket Analysis in the retail industry?
A. Identifying the most important customer demographic
B. Discovering frequently purchased products together for product bundling
C. Predicting weather patterns
D. Forecasting global economic trends
Answer: B
(Market Basket Analysis is used in retail to discover which products are often bought together, which can be used for bundling products or creating promotions.)
15. What is a potential limitation of Market Basket Analysis?
A. It can only be applied to numeric data
B. It often produces too many rules, making interpretation difficult
C. It can only be used for small datasets
D. It cannot analyze relationships between more than two items
Answer: B
(Market Basket Analysis can generate a large number of rules, especially with big datasets, making it difficult to interpret or prioritize the most useful ones.)