- Become competent at implementing regression analysis in Python
- Solve some of the complex data science problems related to predicting outcomes
- Get to grips with various types of regression for effective data analysis
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.
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
About the Author
Luca Massaron is a data scientist and a marketing research director who is specialized in multivariate statistical analysis, machine learning, and customer insight with over a decade of experience in solving real-world problems and in generating value for stakeholders by applying reasoning, statistics, data mining, and algorithms. From being a pioneer of Web audience analysis in Italy to achieving the rank of a top ten Kaggler, he has always been very passionate about everything regarding data and its analysis and also about demonstrating the potential of datadriven knowledge discovery to both experts and non-experts. Favoring simplicity over unnecessary sophistication, he believes that a lot can be achieved in data science just by doing the essentials.
Alberto Boschetti is a data scientist, with an expertise in signal processing and statistics. He holds a Ph.D. in telecommunication engineering and currently lives and works in London. In his work projects, he faces daily challenges that span from natural language processing (NLP) and machine learning to distributed processing. He is very passionate about his job and always tries to stay updated about the latest developments in data science technologies, attending meet-ups, conferences, and other events.
Table of Contents
Chapter 1. Regression – The Workhorse of Data Science
Chapter 2. Approaching Simple Linear Regression
Chapter 3. Multiple Regression in Action
Chapter 4. Logistic Regression
Chapter 5. Data Preparation
Chapter 6. Achieving Generalization
Chapter 7. Online and Batch Learning
Chapter 8. Advanced Regression Methods
Chapter 9. Real-world Applications for Regression Models