In this undertaking, I utilized the widely recognized unsupervised clustering method known as K-Means Clustering. 

Through the use of the elbow method, it was determined that a k-value of 2 was suitable for clustering the data. However, upon further analysis, the model exhibited a high inertia of 237.7572, indicating a poor fit to the data. As a result, I attempted a classification accuracy of 1% with k=2. By adjusting the value of k, I achieved a significantly improved classification accuracy of 62% with k=4. Consequently, it can be determined that k=4 is the optimal number of clusters for this data.

Congratulations on completing the Clustering Project!

 In this project, we are going to understand what a cluster is and how to use it in Python.