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Book Overview & Buying
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Table Of Contents
Regression Analysis with Python
By :
Regression Analysis with Python
By:
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)
Preface
Free Chapter
1. Regression – The Workhorse of Data Science
2. Approaching Simple Linear Regression
3. Multiple Regression in Action
4. Logistic Regression
5. Data Preparation
6. Achieving Generalization
7. Online and Batch Learning
8. Advanced Regression Methods
9. Real-world Applications for Regression Models
Index
