Book Image

Python Data Science Essentials

By : Alberto Boschetti, Luca Massaron
Book Image

Python Data Science Essentials

By: Alberto Boschetti, Luca Massaron

Overview of this book

The book starts by introducing you to setting up your essential data science toolbox. Then it will guide you across all the data munging and preprocessing phases. This will be done in a manner that explains all the core data science activities related to loading data, transforming and fixing it for analysis, as well as exploring and processing it. Finally, it will complete the overview by presenting you with the main machine learning algorithms, the graph analysis technicalities, and all the visualization instruments that can make your life easier in presenting your results. In this walkthrough, structured as a data science project, you will always be accompanied by clear code and simplified examples to help you understand the underlying mechanics and real-world datasets.
Table of Contents (13 chapters)

An overview of unsupervised learning

In all the methods we've seen so far, every sample or observation has its own target label or value. In some other cases, the dataset is unlabelled and, in order to extract the structure of the data, you need an unsupervised approach. In this section, we're going to introduce two methods to perform clustering, as they are among the most used methods for unsupervised learning.


Keep in mind that often, the terms clustering and unsupervised learning are considered synonymous.

The first method that we'll introduce you to is named K-means. In signal processing, it is the equivalent of a vectorial quantization, that is, the selection of the best codeword (from a given codebook) that better approximates the input observation (or a word).

You must provide the algorithm with the K parameter, which is the number of clusters. Sometimes, this might be a limitation because you have to first investigate which is the right K for the current dataset. K-means iterates...