What is the primary business problem addressed using KMeans clustering in the video?
Practical Data Science using Python - K-Means - Data Preparation and Modelling

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1.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
Forecasting sales
Predicting customer churn
Segmenting customers based on spending habits
Optimizing inventory levels
2.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
Which of the following features is NOT considered significant for clustering in the dataset?
Gender
Age
Customer ID
Annual Income
3.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
What is the purpose of using the 'info' function on the data frame?
To check for null values
To visualize data distributions
To plot data points
To calculate statistical summaries
4.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
How is gender data transformed for the KMeans model?
Using one-hot encoding
Mapping male to 1 and female to 0
Normalizing values
Standardizing values
5.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
What is the main focus of the video regarding data analysis?
Detailed exploratory data analysis
Data visualization techniques
KMeans algorithm and its optimization
Handling missing data
6.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
Why is the customer ID dropped from the dataset before clustering?
It contains null values
It is not unique
It is not a numerical feature
It may negatively influence clustering
7.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
What is the purpose of the elbow method in KMeans clustering?
To visualize data distributions
To minimize data preprocessing
To determine the optimal number of clusters
To maximize the silhouette score
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