Book Image

Learning Predictive Analytics with Python

By : Ashish Kumar, Gary Dougan
Book Image

Learning Predictive Analytics with Python

By: Ashish Kumar, Gary Dougan

Overview of this book

Social Media and the Internet of Things have resulted in an avalanche of data. Data is powerful but not in its raw form - It needs to be processed and modeled, and Python is one of the most robust tools out there to do so. It has an array of packages for predictive modeling and a suite of IDEs to choose from. Learning to predict who would win, lose, buy, lie, or die with Python is an indispensable skill set to have in this data age. This book is your guide to getting started with Predictive Analytics using Python. You will see how to process data and make predictive models from it. We balance both statistical and mathematical concepts, and implement them in Python using libraries such as pandas, scikit-learn, and numpy. You’ll start by getting an understanding of the basics of predictive modeling, then you will see how to cleanse your data of impurities and get it ready it for predictive modeling. You will also learn more about the best predictive modeling algorithms such as Linear Regression, Decision Trees, and Logistic Regression. Finally, you will see the best practices in predictive modeling, as well as the different applications of predictive modeling in the modern world.
Table of Contents (19 chapters)
Learning Predictive Analytics with Python
Credits
Foreword
About the Author
Acknowledgments
About the Reviewer
www.PacktPub.com
Preface
A List of Links
Index

Summary


This chapter marks the beginning of the introduction to the algorithms, which are the backbone of predictive modelling. These algorithms are converted into mathematical equations based on the historical data. These equations are the predictive models.

In this chapter, we discussed the simplest and the most widely used predictive modelling technique called linear regression.

Here is a list of things that we learned in this chapter:

  • Linear regression assumes a linear relationship between an output variable and one or more predictor variables. The one with a single predictor variable is called a simple linear regression while the one with multiple variables is called multiple linear regression.

  • The coefficients of the linear relationship (model) are estimated using the least sum of squares method.

  • In Python, statsmodel.api and scikit-learn are the two methods to implement Python.

  • The coefficient of determination, R2, is a good way to gauge the efficiency of the model in explaining the error...