From the cleaned dataset, I used a dataset containing patent-related information, including citation counts and other patent attributes. This dataset serves as labeled data, though for unsupervised clustering, removed non-numeric labels, using only citation-related numeric columns. Then using StandardScaler from Sklearn to normalize the citation data. This process ensures that each column has a mean of 0 and a standard deviation of 1, which is crucial for ensuring that all features contribute equally to the clustering process. PCA was applied twice: once with 2 components and once with 3 components. This helped in reducing the dimensionality of the data for 2D and 3D visualizations while retaining most of the variance.