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

Making sense of result parameters


Apart from the R2 statistic, there are other statistics and parameters that one needs to look at in order to do the following:

  1. Select some variables and discard others for the model.

  2. Assess the relationship between the predictor and output variable and check whether a predictor variable is significant in the model or not.

  3. Calculate the error in the values predicted by the selected model.

Let us now see some of the statistics which helps to address the issues discussed earlier.

p-values

One thing to realize here is that the calculation of a and β are estimates and not the exact calculations. Whether their values are significant or not need to be tested using a hypothesis test.

The hypothesis tests whether the value of β is non-zero or not; in other words whether there is a sufficient correlation between X and yact. If there is, the β will be non-zero.

In the equation, y= a +β*x, if we put β=0, there will be no relation between y and x. Hence the hypothesis test is...