1|Page
QMB3302 UF FALL Final Exam Version
1,2&3 (3 Latest Versions) Newest 2025/2026
With Complete Questions And Correct
Answers |Already Graded A+||Brand New
Version!
The correct number of clusters in Hierarchical clustering
can be determined precisely using approaches such as
silhouette scores (True or False) - . . ANSWER ✔
✔False
In K Means clustering, the analyst does not need to
determine the number of clusters (K), these are always
derived analytically using the kmeans algorithm. (True or
False) - . . ANSWER ✔ ✔False
One big difference between the unsupervised approaches
in this module, and the supervised approaches in prior
modules: Unsupervised models do not have a target
variable (Y). This make is difficult to know when they are
"right" or correct. (True or False) - . . ANSWER ✔
✔True
,2|Page
According to the documentation, a silhouette scores of 1 ia
- . . ANSWER ✔ ✔The best score
According to the documentation, a silhouette score of -1 is
- . . ANSWER ✔ ✔The worst score
Select all that apply. Imagine you have a data set with
columns/inputs for customers:
Column 1 = Customer ID (a number)
Column 2 = Sales (a dollar value)
Column 3= Frequency (a number)
Column 4 = Satisfaction (a number)
You would like to understand the impact of Frequency on
customer Satisfaction. What types of approaches could you
use?
,3|Page
Note that the type of data is brackets () after the column
name. - . . ANSWER ✔ ✔Decision tree, random forest,
linear regression
Select all that apply. Imagine you have a dataset with the
following columns (inputs) for a set of customers.
Column 1 = Customer ID
Column 2 = Distance to Store
Column 3= Yearly spend
Column 4 = Likelihood to return (a survey response that
indicates a customer is likely to shop again)
What kind of approaches could you use to understand
more about these customers? Why? - . . ANSWER ✔
✔Regression - to udnerstand the effect of one or more
variables on the others
Clustering-to develop groups of customers that have
similar patterns
, 4|Page
What is the purpose of the following code?
from sklearn.preprocessing import StandardScaler
scale = StandardScaler()
rfm_std = scale.fit_transform(df) - . . ANSWER ✔ ✔To
standardize the data
The elbow method provides an exact number of clusters
for a kmeans algorithm. (True or False) - . . ANSWER ✔
✔False
Hierarchical clustering is more powerful than Kmeans, as
it allows the researcher to determine the exact number of
clusters to use in the analysis. (True or False) - . .
ANSWER ✔ ✔False
In kmeans- the algorithm has multiple iterations. If we
have a simple 2d problem, and a k =2, it begins by
assigning the first centroids to - . . ANSWER ✔ ✔A
random initial starting point
In kmeans- the algorithm has multiple iterations. If we
have a simple 2d problem, and a k =2. After the initial
QMB3302 UF FALL Final Exam Version
1,2&3 (3 Latest Versions) Newest 2025/2026
With Complete Questions And Correct
Answers |Already Graded A+||Brand New
Version!
The correct number of clusters in Hierarchical clustering
can be determined precisely using approaches such as
silhouette scores (True or False) - . . ANSWER ✔
✔False
In K Means clustering, the analyst does not need to
determine the number of clusters (K), these are always
derived analytically using the kmeans algorithm. (True or
False) - . . ANSWER ✔ ✔False
One big difference between the unsupervised approaches
in this module, and the supervised approaches in prior
modules: Unsupervised models do not have a target
variable (Y). This make is difficult to know when they are
"right" or correct. (True or False) - . . ANSWER ✔
✔True
,2|Page
According to the documentation, a silhouette scores of 1 ia
- . . ANSWER ✔ ✔The best score
According to the documentation, a silhouette score of -1 is
- . . ANSWER ✔ ✔The worst score
Select all that apply. Imagine you have a data set with
columns/inputs for customers:
Column 1 = Customer ID (a number)
Column 2 = Sales (a dollar value)
Column 3= Frequency (a number)
Column 4 = Satisfaction (a number)
You would like to understand the impact of Frequency on
customer Satisfaction. What types of approaches could you
use?
,3|Page
Note that the type of data is brackets () after the column
name. - . . ANSWER ✔ ✔Decision tree, random forest,
linear regression
Select all that apply. Imagine you have a dataset with the
following columns (inputs) for a set of customers.
Column 1 = Customer ID
Column 2 = Distance to Store
Column 3= Yearly spend
Column 4 = Likelihood to return (a survey response that
indicates a customer is likely to shop again)
What kind of approaches could you use to understand
more about these customers? Why? - . . ANSWER ✔
✔Regression - to udnerstand the effect of one or more
variables on the others
Clustering-to develop groups of customers that have
similar patterns
, 4|Page
What is the purpose of the following code?
from sklearn.preprocessing import StandardScaler
scale = StandardScaler()
rfm_std = scale.fit_transform(df) - . . ANSWER ✔ ✔To
standardize the data
The elbow method provides an exact number of clusters
for a kmeans algorithm. (True or False) - . . ANSWER ✔
✔False
Hierarchical clustering is more powerful than Kmeans, as
it allows the researcher to determine the exact number of
clusters to use in the analysis. (True or False) - . .
ANSWER ✔ ✔False
In kmeans- the algorithm has multiple iterations. If we
have a simple 2d problem, and a k =2, it begins by
assigning the first centroids to - . . ANSWER ✔ ✔A
random initial starting point
In kmeans- the algorithm has multiple iterations. If we
have a simple 2d problem, and a k =2. After the initial
