Regression Analysis with Python

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

Luca Massaron, Alberto Boschetti

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

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Book Details

ISBN 139781785286315
Paperback312 pages

Book 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.

Table of Contents

Chapter 1: Regression – The Workhorse of Data Science
Regression analysis and data science
Python for data science
Python packages and functions for linear models
Summary
Chapter 2: Approaching Simple Linear Regression
Defining a regression problem
Starting from the basics
Extending to linear regression
Minimizing the cost function
Summary
Chapter 3: Multiple Regression in Action
Using multiple features
Revisiting gradient descent
Estimating feature importance
Interaction models
Polynomial regression
Summary
Chapter 4: Logistic Regression
Defining a classification problem
Defining a probability-based approach
Revisiting gradient descent
Multiclass Logistic Regression
An example
Summary
Chapter 5: Data Preparation
Numeric feature scaling
Qualitative feature encoding
Numeric feature transformation
Missing data
Outliers
Summary
Chapter 6: Achieving Generalization
Checking on out-of-sample data
Greedy selection of features
Regularization optimized by grid-search
Stability selection
Summary
Chapter 7: Online and Batch Learning
Batch learning
Online mini-batch learning
Summary
Chapter 8: 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
Chapter 9: 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

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

Authors

Table of Contents

Chapter 1: Regression – The Workhorse of Data Science
Regression analysis and data science
Python for data science
Python packages and functions for linear models
Summary
Chapter 2: Approaching Simple Linear Regression
Defining a regression problem
Starting from the basics
Extending to linear regression
Minimizing the cost function
Summary
Chapter 3: Multiple Regression in Action
Using multiple features
Revisiting gradient descent
Estimating feature importance
Interaction models
Polynomial regression
Summary
Chapter 4: Logistic Regression
Defining a classification problem
Defining a probability-based approach
Revisiting gradient descent
Multiclass Logistic Regression
An example
Summary
Chapter 5: Data Preparation
Numeric feature scaling
Qualitative feature encoding
Numeric feature transformation
Missing data
Outliers
Summary
Chapter 6: Achieving Generalization
Checking on out-of-sample data
Greedy selection of features
Regularization optimized by grid-search
Stability selection
Summary
Chapter 7: Online and Batch Learning
Batch learning
Online mini-batch learning
Summary
Chapter 8: 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
Chapter 9: 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

Book Details

ISBN 139781785286315
Paperback312 pages
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