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Regression Analysis with Python

Regression Analysis with Python

By : Luca Massaron , Alberto Boschetti
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Regression Analysis with Python

Regression Analysis with Python

3 (4)
By: Luca Massaron , Alberto Boschetti

Overview of this book

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.
Table of Contents (11 chapters)
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10
Index

Conventions

In this book, you will find a number of text styles that distinguish between different kinds of information. Here are some examples of these styles and an explanation of their meaning.

Code words in text, database table names, folder names, filenames, file extensions, pathnames, dummy URLs, user input, and Twitter handles are shown as follows: "When inspecting the linear model, first check the coef_ attribute."

A block of code is set as follows:

from sklearn import datasets 
iris = datasets.load_iris()  

Since we will be using IPython Notebooks along most of the examples, expect to have always an input (marked as In:) and often an output (marked Out:) from the cell containing the block of code. On your computer you have just to input the code after the In: and check if results correspond to the Out: content:

In: clf.fit(X, y)
Out: SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=0.0, kernel='rbf', max_iter=-1, probability=False, random_state=None, shrinking=True, tol=0.001, verbose=False) 

When a command should be given in the terminal command line, you'll find the command with the prefix $>, otherwise, if it's for the Python REPL it will be preceded by >>>:

    $>python
    >>> import sys

>>> print sys.version_info

New terms and important words are shown in bold. Words that you see on the screen, for example, in menus or dialog boxes, appear in the text like this: "Go to the Databases section and create a new database using the UTF collation."

Note

Warnings or important notes appear in a box like this.

Tip

Tips and tricks appear like this.

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Regression Analysis with Python
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