Unsupervised Machine Learning: Clustering Analysis – Towards Data Science


Up to know, we have explored just supervised Machine Learning algorithms and techniques to develop models where the data had label previously known. In other words, our data had some target variables with specific values that we used to train our models. However, when dealing with real-world problems, most of the time, data will not come with predefined labels, so we will want to develop machine learning models that can classify correctly this data, by finding by themselves some commonality in the features, that will be used to predict the classes on new data. In summary, the main goal is to study the intrinsic (and commonly hidden) structure of the data. This techniques can be condensed in two main types of problems that unsupervised learning tries to solve.

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