Book Image

Mastering Python Data Analysis

By : Magnus Vilhelm Persson
Book Image

Mastering Python Data Analysis

By: Magnus Vilhelm Persson

Overview of this book

Python, a multi-paradigm programming language, has become the language of choice for data scientists for data analysis, visualization, and machine learning. Ever imagined how to become an expert at effectively approaching data analysis problems, solving them, and extracting all of the available information from your data? Well, look no further, this is the book you want! Through this comprehensive guide, you will explore data and present results and conclusions from statistical analysis in a meaningful way. You’ll be able to quickly and accurately perform the hands-on sorting, reduction, and subsequent analysis, and fully appreciate how data analysis methods can support business decision-making. You’ll start off by learning about the tools available for data analysis in Python and will then explore the statistical models that are used to identify patterns in data. Gradually, you’ll move on to review statistical inference using Python, Pandas, and SciPy. After that, we’ll focus on performing regression using computational tools and you’ll get to understand the problem of identifying clusters in data in an algorithmic way. Finally, we delve into advanced techniques to quantify cause and effect using Bayesian methods and you’ll discover how to use Python’s tools for supervised machine learning.
Table of Contents (15 chapters)
Mastering Python Data Analysis
Credits
About the Authors
About the Reviewer
www.PacktPub.com
Preface

Chapter 7. Supervised and Unsupervised Learning

The amount of data collected for various purposes in society has increased enormously in the last few decades. Machine learning is a way of making sense of all this data by leveraging what we know about the data. In the generalized picture of machine learning, the computer first learns from a given dataset (training) and creates a generalized model to represent it. With this model, it is possible to predict various outcomes, results, and groupings (classes). In this chapter, we will cover the following topics:

  • Linear regression with machine learning algorithms

  • Clustering with machine learning algorithms

  • Feature selection—a preprocessing method to select what is most important

  • Classification with different machine learning algorithms and kernels

Before getting started, I will give you a brief introduction to machine learning and the package that we will use: Scikit-learn.