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.

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

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

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

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