Machine Learning Clustering Expert

@Iwori

Developing robust clustering models for high-dimensional numerical datasets is exactly the kind of Scikit-learn challenge I specialize in. My approach starts with intensive data preprocessing, focusing on feature scaling via StandardScaler and dimensionality reduction through PCA if necessary to handle multicollinearity. I'll execute a pipeline that evaluates K-means, Hierarchical, and DBSCAN using the Silhouette Score and Davies-Bouldin Index to determine the optimal cluster density and grouping logic. For the final model, I'll leverage NumPy and Pandas for feature engineering, followed by cluster profiling using T-SNE or UMAP to visualize high-dimensional boundaries in a readable 2D format. I'll also include an Elbow Method analysis and a centroid interpretation report to ensure the results align with your business logic. I can start immediately.