Chapter 1

Question 1

What is predictive analytics?

Answer 1

Use of data, statistical analysis, and models to predict future outcomes and guide decision-making.

Question 2

What are the three main branches of business analytics?

Answer 2

• Descriptive Analytics
• Predictive Analytics
• Prescriptive Analytics

Question 3

What is the purpose of descriptive analytics?

Answer 3

To understand past and present trends.

Question 4

What is the purpose of prescriptive analytics?

Answer 4

To recommend actions using optimization techniques.

Question 5

What is data mining?

Answer 5

Central to predictive analytics, utilizing both supervised and unsupervised methods.

Question 6

What are the two types of learning in data mining?

Answer 6

• Supervised Learning
• Unsupervised Learning

Question 7

What does supervised learning include?

Answer 7

• Classification
• Prediction

Question 8

What does unsupervised learning include?

Answer 8

• Clustering
• Association Rules
• Data Reduction

Question 9

What is the importance of data exploration and visualization?

Answer 9

Helps uncover patterns, trends, and relationships.

Question 10

What are the critical steps in the data mining process?

Answer 10

• Define/understand the purpose
• Obtain data
• Explore, clean, and pre-process the data
• Reduce or partition the data
• Specify the task
• Choose techniques
• Iteratively implement and fine-tune models
• Assess and compare results
• Deploy the best model

Question 11

What are numeric variables?

Answer 11

• Continuous (e.g., revenue)
• Integer (e.g., transactions)

Question 12

What are categorical variables?

Answer 12

• Ordered (e.g., low, medium, high)
• Unordered (e.g., gender)

Question 13

What is the significance of binary target variables in supervised classification tasks?

Answer 13

Represent yes/no outcomes.

Question 14

True or False: Data mining often applies to the entire database rather than a sample.

Answer 14

False

Question 15

What is the role of pre-processing in data mining?

Answer 15

It is critical for cleaning and exploring data.

Question 16

Describe the difference between supervised and unsupervised learning.

Answer 16

Supervised learning predicts outcomes based on labeled data; unsupervised learning identifies patterns without labeled outcomes.

Question 17

What is the process of data mining and predictive analytics?

Answer 17

1. Define/understand the purpose.
2. Obtain data (e.g., random sampling).
3. Explore, clean, and pre-process the data.
4. Reduce or partition the data (if supervised learning is used).
5. Specify the task (classification, clustering, etc.).
   6.Choose techniques (e.g., regression, neural networks, CART).
6. Iteratively implement and fine-tune models.
7. Assess and compare results.
8. Deploy the best model.