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

Introducing linear regression


The simplest form of linear regression is given by the relation y = k+ k , where k is called intercept, that is, the value of y when x=0 and k is the slope. A general expression for this could be found by thinking of each point as the preceding relation plus an error ε. This would then look, for N points, as follows:

We can express this in matrix form:

Here, the various matrices/vectors are represented as follows:

Performing the multiplication and addition of the matrix and vectors should yield the same set of equations that are defined here. The goal of the regression is to estimate the parameters, k, in this case. There are many types of parameter estimation methods—ordinary least squares being one of the most common—but there are also maximum likelihood, Bayesian, mixed model, and several others. In ordinary least-squares minimization, the square of the residuals are minimized, that is, rTr is minimized (T denotes the transpose and r denotes...