this introduced range of unsupervised learning algorithms that can be applied for exploratory data analysis and preprocessing having the right representation of the data is often crucial for supervised or unsupervised learning to succeedand preprocessing and decomposition methods play an important part in data preparation decompositionmanifold learningand clustering are essential tools to further your understanding of your dataand can be the only ways to make sense of your data in the absence of supervision information even in supervised settingexploratory tools are important for better understanding of the properties of the data often it is hard to quantify the usefulness of an unsupervised algorithmthough this shouldn' deter you from using them to gather insights from your data with these methods under your beltyou are now equipped with all the essential learning algorithms that machine learning practitioners use every day we encourage you to try clustering and decomposition methods both on twodimensional toy data and on real-world datasets included in scikit-learnlike the digitsirisand cancer datasets unsupervised learning and preprocessing