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.

Discuss the importance of exploratory data analysis (EDA) in the K-means clustering project.

How is the K-means model trained with the dataset?

What does the inertia value represent in K-means clustering?

What is the purpose of the elbow method in the context of K-means clustering?