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Worksheets

Real-Life Applications of Data Mining

Total questions: 20

Worksheet time: 10mins

Name
Class
Date
1.

Which of the following is a primary use of data mining in healthcare?

a)

Predicting movie box office sales

b)

Identifying disease outbreaks and patient risk factors

c)

Calculating stock market indices

d)

Designing video games

2.

Which technique is commonly used in fraud detection to identify unusual patterns in financial transactions?

a)

Clustering

b)

Regression

c)

Association rule mining

d)

Time series forecasting

3.

In social media sentiment analysis, what is the main goal?

a)

To count the number of posts

b)

To determine the emotional tone behind user comments

c)

To increase the number of followers

d)

To block spam accounts

4.

Which of the following best describes predictive analytics in sports?

a)

Analyzing past games for entertainment

b)

Predicting future player performance and game outcomes

c)

Creating new sports equipment

d)

Broadcasting live matches

5.

Which data mining application is used in education to identify students at risk of failing?

a)

Market basket analysis

b)

Early warning systems using predictive modeling

c)

Image recognition

d)

Weather forecasting

6.

A grocery store retailer is trying to decide whether to put bread on sale. This is related to which data mining task?

a)

Association

b)

Outlier analysis

c)

Summarization

d)

Prediction

7.

Which of the following is a key challenge in fraud detection using data mining?

a)

Lack of data

b)

Evolving fraud patterns

c)

Too many labeled examples

d)

Unlimited computational resources

8.

Which metric is commonly used to evaluate the accuracy of sentiment analysis models?

a)

Mean squared error

b)

Precision, recall, and F1-score

c)

Gini index

d)

Support

9.

In sports analytics, which variable might be predicted using regression analysis?

a)

Player jersey color

b)

Number of goals scored in a season

c)

Stadium seating arrangement

d)

Team mascot

10.

Which of the following is an example of data mining in education?

a)

Predicting student dropout rates

b)

Scheduling school buses

c)

Designing classroom furniture

d)

Printing textbooks

11.

Which data mining technique is often used to detect anomalies in healthcare data?

a)

Anomaly detection

b)

Text summarization

c)

Web scraping

d)

Data encryption

12.

Which of the following is a common data source for social media sentiment analysis?

a)

Medical records

b)

Tweets and Facebook posts

c)

Weather reports

d)

Satellite images

13.

Which mathematical concept is often used in predictive analytics to estimate probabilities?

a)

Calculus

b)

Probability theory

c)

Geometry

d)

Trigonometry

14.

Which of the following is a benefit of using data mining in fraud detection?

a)

Increases manual workload

b)

Detects fraudulent activities faster and more accurately

c)

Reduces the need for data

d)

Guarantees zero fraud

15.

Which of the following is a limitation of sentiment analysis in social media?

a)

It can only analyze images

b)

Difficulty in understanding sarcasm and slang

c)

It always gives 100% accurate results

d)

It requires no data

16.

Which predictive analytics technique is commonly used in sports to optimize team strategies?

a)

Linear regression

b)

Genetic algorithms

c)

Neural networks

d)

All of the above

17.

Which of the following is a goal of education data mining?

a)

To improve teaching methods and student learning outcomes

b)

To increase cafeteria food options

c)

To reduce school holidays

d)

To design sports uniforms

18.

A college professor wishes to reach a certain level of savings before her retirement. This is related to which data mining task?

a)

Clustering

b)

Classification

c)

Association

d)

Regression

19.

Which data mining method is often used to group students with similar learning behaviors?

a)

Clustering

b)

Classification

c)

Regression

d)

Association rule mining

20.

Which of the following best describes the use of association rule mining in fraud detection?

a)

Identifying common itemsets in shopping carts

b)

Discovering unusual combinations of transactions that may indicate fraud

c)

Predicting weather patterns

d)

Analyzing DNA sequences