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Key benefits

Become competent at implementing regression analysis in Python

Solve some of the complex data science problems related to predicting outcomes

Get to grips with various types of regression for effective data analysis

Description

Regression is the process of learning relationships between inputs and continuous outputs from example data, which enables predictions for novel inputs. There are many kinds of regression algorithms, and the aim of this book is to explain which is the right one to use for each set of problems and how to prepare real-world data for it. With this book you will learn to define a simple regression problem and evaluate its performance. The book will help you understand how to properly parse a dataset, clean it, and create an output matrix optimally built for regression. You will begin with a simple regression algorithm to solve some data science problems and then progress to more complex algorithms. The book will enable you to use regression models to predict outcomes and take critical business decisions. Through the book, you will gain knowledge to use Python for building fast better linear models and to apply the results in Python or in any computer language you prefer.

What you will learn

* Format a dataset for regression and evaluate its performance

* Apply multiple linear regression to real-world problems

* Learn to classify training points

* Create an observation matrix, using different techniques of data analysis and cleaning

* Apply several techniques to decrease (and eventually fix) any overfitting problem

* Learn to scale linear models to a big dataset and deal with incremental data

What do you get with eBook?

Instant access to your Digital eBook purchase

Download this book in EPUB and PDF formats

Access this title in our online reader with advanced features

DRM FREE - Read whenever, wherever and however you want

Frequently bought together

Python Data Visualization Cookbook (Second Edition)

Mex$1045.99

Mex$1004.99

Learning Predictive Analytics with Python

Mex$1128.99

Total Mex$ 3,179.97

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Amazon Customer Jul 23, 2017

This book will teach you the basics of Machine Learning - with focus on regression techniques, and show how to apply it to real-world problems. It starts from the simplest statistical concept of correlation, then goes into linear regression and gradually you'll find yourself reading about advanced techniques like Gradient Boosting. The book doesn't just show how to use off-the-shelf solutions like scikit-learn or statsmodels, but also gives enough theoretical grounds to understand what's going on in the library. Of course, it's all illustrated with nice examples.I recommend this book to anyone who would like to start with Machine Learning in Python

Amazon Verified review

Abacus Nov 08, 2018

For the targeted audience, the book teaches a lot about Python and regression analysis.Their coverage of the latter is surprisingly deep; especially within chapters 6 & 8 where they cover regularization methods and other advanced regression methods. Theoretical subjects such as how to treat outliers, overfitting vs underfitting, bias vs. variance, and many other topics are covered in a top-notch professorial manner.Their specific explanation of what the various main packages do is excellent. The numerous examples using either statsmodels or scikit-learn are very good.For my part, the book had a somewhat limited use because I work mainly with longitudinal data (econometrics time series). Meanwhile, the authors demonstrated regressions mainly using panel data (at the end they show a time series analysis which is not a full fledge multiple regression). Also, I found the coding at times burdensome (standardizing variables and graphs demanded a lot of codes). This may be in part due to the Python language itself. I come from R that is far more efficient for regression modeling.The book has a few questionable statements that do not detract from its overall quality. On pg. 39, the authors indicate that variables have to be standardized to get representative correlations. That’s inaccurate. I have tested this assumption with 10 different macroeconomic variables on different scales. And, I got the exact same correlation matrix whether the variables were in their original transformation or standardized. On pg. 51, the authors indicate that kurtosis is centered around 0. Within Python statsmodels, it is actually centered around 3. On page 132 & 133, when they either standardize or normalize variables to run regressions, based on the coding it seems that they only do so for the Xs variables and not for Y. If that is the case, that’s not a good model specification. On pg. 216, the explanation of stepwise forward selection seems inaccurate (in step 2, they indicate all other variables are projected on the first one selected; but, instead all other variables are correlated to the residual of the model using the first selected variables. And, you select the variable that has the highest absolute correlation with the mentioned residuals).In conclusion, this is a good book on the subject. Just like for any programing language, you don’t learn from just a single source. You will find yourself studying books, on-line documentation, YouTube videos, and maybe take online courses.

B. Henderson Jul 12, 2023

This is actually a good book and fairly well written for a Packt, covering the reasons as well as the code. Unfortunately, the code is from 2016 so you will spend a lot of time looking through docs to replicate their methods. Also, Packt no longer supports this book, so code and errata are not available. Once good, old junk now, but useful for learning how things are/were done with less developed packages.

Sean McGhee May 24, 2017

Filled with typos: F statistic had a "|" instead of a "+". For a math book this is impossible to learn from when there are so many error.s

Regression Analysis with Python: Discover everything you need to know about the art of regression analysis with Python, and change how you view data

What do you get with eBook?