Book Image

Regression Analysis with Python

By : Luca Massaron, Alberto Boschetti
4 (1)
Book Image

Regression Analysis with Python

4 (1)
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.

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Regression Analysis with Python
Luca Massaron | Alberto Boschetti
Feb, 2016 | 312 pages
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Table of Contents (16 chapters)
Regression Analysis with Python
Regression Analysis with Python
Credits
Credits
About the Authors
About the Authors
About the Reviewers
About the Reviewers
www.PacktPub.com
www.PacktPub.com
Preface
Preface
Free Chapter
1
Regression – The Workhorse of Data Science
Regression – The Workhorse of Data Science
Regression analysis and data science
Python for data science
Python packages and functions for linear models
Summary
2
Approaching Simple Linear Regression
Approaching Simple Linear Regression
Defining a regression problem
Starting from the basics
Extending to linear regression
Minimizing the cost function
Summary
3
Multiple Regression in Action
Multiple Regression in Action
Using multiple features
Revisiting gradient descent
Estimating feature importance
Interaction models
Polynomial regression
Summary
4
Logistic Regression
Logistic Regression
Defining a classification problem
Defining a probability-based approach
Revisiting gradient descent
Multiclass Logistic Regression
An example
Summary
5
Data Preparation
Data Preparation
Numeric feature scaling
Qualitative feature encoding
Numeric feature transformation
Missing data
Outliers
Summary
6
Achieving Generalization
Achieving Generalization
Checking on out-of-sample data
Greedy selection of features
Regularization optimized by grid-search
Stability selection
Summary
7
Online and Batch Learning
Online and Batch Learning
Batch learning
Online mini-batch learning
Summary
8
Advanced Regression Methods
Advanced Regression Methods
Least Angle Regression
Bayesian regression
SGD classification with hinge loss
Regression trees (CART)
Bagging and boosting
Gradient Boosting Regressor with LAD
Summary
9
Real-world Applications for Regression Models
Real-world Applications for Regression Models
Downloading the datasets
A regression problem
An imbalanced and multiclass classification problem
A ranking problem
A time series problem
Summary
Index
Index

Downloading the datasets

In this section of the book, we will download all the datasets that are going to be used in the examples in this chapter. We chose to store them in separate subdirectories of the same folder where the IPython Notebook is contained. Note that some of them are quite big (100+ MB).

Tip

We would like to thank the maintainers and the creators of the UCI dataset archive. Thanks to such repositories, modeling and achieving experiment repeatability are much easier than before. The UCI archive is from Lichman, M. (2013). UCI Machine Learning Repository [http://archive.ics.uci.edu/ml]. Irvine, CA: University of California, School of Information and Computer Science.

For each dataset, we first download it, and then we present the first couple of lines. First, this will help demonstrate whether the file has been correctly downloaded, unpacked, and placed into the right location; second, it will show the structure of the file itself (header, fields, and so on):

In:
try:
    import...
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