Regression Analysis with Python - Luca Massaron.pdf

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Regression Analysis with Python
Table of Contents
Regression Analysis with Python
Credits
About the Authors
About the Reviewers
www.PacktPub.com
eBooks, discount offers, and more
Why subscribe?
Preface
What this book covers
What you need for this book
Who this book is for
Conventions
Reader feedback
Customer support
Downloading the example code
Downloading the color images of this book
Errata
Piracy
Questions
1. Regression – The Workhorse of Data Science
Regression analysis and data science
Exploring the promise of data science
The challenge
The linear models
What you are going to find in the book
Python for data science
Installing Python
Choosing between Python 2 and Python 3
Step-by-step installation
Installing packages
Package upgrades
Scientific distributions
Introducing Jupyter or IPython
Python packages and functions for linear models
NumPy
SciPy
Statsmodels
Scikit-learn
Summary
2. Approaching Simple Linear Regression
Defining a regression problem
Linear models and supervised learning
Reflecting on predictive variables
Reflecting on response variables
The family of linear models
Preparing to discover simple linear regression
Starting from the basics
A measure of linear relationship
Extending to linear regression
Regressing with Statsmodels
The coefficient of determination
Meaning and significance of coefficients
Evaluating the fitted values
Correlation is not causation
Predicting with a regression model
Regressing with Scikit-learn
Minimizing the cost function
Explaining the reason for using squared errors
Pseudoinverse and other optimization methods
Gradient descent at work

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