Book Image

Python for Data Science For Dummies - Second Edition

By : John Paul Mueller, Luca Massaron
Book Image

Python for Data Science For Dummies - Second Edition

By: John Paul Mueller, Luca Massaron

Overview of this book

Python is a general-purpose programming language created in the late 1980s — and named after Monty Python — that's used by thousands of people to do things from testing microchips at Intel to powering Instagram to building video games with the PyGame library. The book begins by discussing how Python can make data science easy. You’ll learn how to work with the Anaconda tool suite that makes coding in Python easy. You’ll also learn to write code using Google Colab. As you progress, you'll discover how to perform interesting calculations and data manipulations using various Python libraries, such as pandas and NumPy. You’ll learn how to create data visualizations with MatPlotLib. While learning the advanced concepts, you’ll learn how to wrangle data by using techniques, such as hierarchical clustering. Finally, you’ll learn how to work with decision trees and use machine learning to make predictions. By the end of the book, you’ll have the skills and the knowledge that’s needed to write code in Python and extract information from data.
Table of Contents (13 chapters)
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Chapter 15



Bullet Exploring the potentialities of unsupervised clustering

Bullet Making K-means work with small and big data

Bullet Trying DBScan as an alternative option

One of the basic abilities that humans have exercised since primitive times is to divide the known world into separate classes where individual objects share common features deemed important by the classifier. Starting with primitive cave dwellers classifying the natural world they lived in, distinguishing plants and animals useful or dangerous for their survival, we arrive at modern times in which marketing departments classify consumers into target segments and then act with proper marketing plans.

Classifying is crucial to our process of building new knowledge because, by gathering similar objects, we can:

  • Mention all the items in a class by the same denomination
  • Summarize relevant features by an exemplificative class type
  • Associate particular actions or recall specific knowledge automatically