normal pca anomaly detection on the test set ica anomaly detection on the test set dictionary learning anomaly detection on the test set conclusion clustering mnist digits dataset data preparation clustering algorithms -means -means inertia evaluating the clustering results -means accuracy -means and the number of principal components -means on the original dataset hierarchical clustering agglomerative hierarchical clustering the dendrogram evaluating the clustering results dbscan dbscan algorithm applying dbscan to our dataset hdbscan conclusion group segmentation lending club data data preparation transform string format to numerical format impute missing values engineer features select final set of features and perform scaling designate labels for evaluation goodness of the clusters -means application hierarchical clustering application hdbscan application conclusion vi table of contents