Segmenting Mall Customers

The video tutorial guides viewers through the process of using K-means clustering to segment customers in a shopping mall dataset. It covers data exploration, cleaning, and visualization techniques, and explains how to determine the optimal number of clusters using the elbow method. The tutorial emphasizes targeting specific customer segments for marketing strategies based on spending behavior and demographic information.

What is the primary goal of using K-means clustering in the mall customer project?

• To predict future sales

• To segment customers into groups

• To increase the number of products

• To reduce operational costs

Which of the following is NOT a demographic feature used in the dataset?

• Customer ID

• Gender

• Age

• Annual Income

Why is the elbow method used in the K-means clustering process?

• To visualize the clusters

• To determine the optimal number of clusters

• To calculate the average spending score

• To clean the dataset

What is the significance of the 'spending score' in the dataset?

• It represents the customer's gender

• It shows the customer's annual income

• It measures how much a customer likes to spend

• It indicates the customer's age

Which visualization technique is used to display the clusters in the project?

• Bar chart

• Line graph

• Scatter plot

• Histogram

What is the main focus when targeting a specific customer segment?

• Customers with low spending scores

• Customers with high annual income and high spending scores

• Customers with the lowest age

• Customers with the highest number of visits

How can the segmented customer data be saved for future use?

• By printing it on paper

• By saving it as a new CSV file

• By emailing it to the manager

• By storing it in a text document