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 17

Exploring Four Simple and Effective Algorithms


Bullet Using linear and logistic regression

Bullet Understanding Bayes’ theorem and using it for naive classification

Bullet Predicting on the basis of cases being similar with KNN

In this new part of the book, you start to explore all the algorithms and tools necessary for learning from data (training a model with data) and being capable of predicting a numeric estimate (for example, house pricing) or a class (for instance, the species of an Iris flower) given any new example that you didn’t have before. In this chapter, you start with the simplest algorithms and work toward those that are more complex. The four algorithms in this chapter represent a good starting point for any data scientist.

Remember You don’t have to type the source code for this chapter manually. In fact, it’s a lot easier if you use the downloadable source (see the Introduction for download instructions). The source code for this chapter...