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
- 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
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