No-code ML with RapidMiner: Performing Clustering Analysis

Clustering models work with unlabeled data finding logical groupings in data and are often used for social media ad targeting and document discovery. In this course explore the clustering unsupervised learning technique. Next retrieve data from the repository into your process and use Turbo Prep to clean and preprocess the data for clustering analysis. Then use Auto Model to train k-means and x-means clustering models on your data and evaluate and visualize the models created. Finally create your own analytics process for k-means clustering evaluate your model using the Davies-Bouldin score use principal component analysis (PCA) to better visualize the clusters found in your data and determine the ideal number of clusters by using hyperparameter tuning. When you are finished with this course you will be able to fit and evaluate clustering models on your data and visualize clusters with data points plotted using principal components.