The data is partitioned into different numbers of clusters using the k-means method, and the results of this clustering are plotted on a graph. Next, the elbow method is used to determine the optimal number of clusters. Hierarchical clustering is also applied in a similar manner, and the number of clusters is determined using dendrograms. Finally, the data is subjected to dimensionality reduction using the PCA method, and the results are plotted in new dimensions.