K-Means Model with 4 ClustersK-Means Model with 4 Clusters


  1. Evaluate the KMeans with 4 clusters;
  2. Fit the algorithm;

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!

Everything was clear?

Section 1. Chapter 11

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