Practical Data Science using Python - K-Means - Data Preparation and Modelling

The video tutorial explains KMeans clustering using a simplified business problem. It covers data preparation, library imports, and data analysis, focusing on building and optimizing a KMeans model. The tutorial uses elbow and silhouette methods to determine the optimal number of clusters.

What is the primary business problem addressed using KMeans clustering in the video?

• Forecasting sales

• Predicting customer churn

• Segmenting customers based on spending habits

• Optimizing inventory levels

Which of the following features is NOT considered significant for clustering in the dataset?

• Gender

• Age

• Customer ID

• Annual Income

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

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

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

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

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

Which method is used alongside the elbow method to determine the optimal number of clusters?

• Hierarchical clustering

• Principal component analysis

• Silhouette score

• K-nearest neighbors

What is the goal when using the silhouette score in clustering?

• To minimize the score

• To maximize the score

• To equalize the score

• To ignore the score

What is the final decision on the optimal number of clusters based on the video?

• 6 clusters

• 3 clusters

• 8 clusters

• 5 clusters