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Learning Predictive Analytics with Python

You're reading from   Learning Predictive Analytics with Python Gain practical insights into predictive modelling by implementing Predictive Analytics algorithms on public datasets with Python

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Product type Paperback
Published in Feb 2016
Publisher
ISBN-13 9781783983261
Length 354 pages
Edition 1st Edition
Languages
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Authors (2):
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Ashish Kumar Ashish Kumar
Author Profile Icon Ashish Kumar
Ashish Kumar
Gary Dougan Gary Dougan
Author Profile Icon Gary Dougan
Gary Dougan
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Toc

Table of Contents (12) Chapters Close

Preface 1. Getting Started with Predictive Modelling FREE CHAPTER 2. Data Cleaning 3. Data Wrangling 4. Statistical Concepts for Predictive Modelling 5. Linear Regression with Python 6. Logistic Regression with Python 7. Clustering with Python 8. Trees and Random Forests with Python 9. Best Practices for Predictive Modelling A. A List of Links
Index

Correlation


Another statistical idea which is very basic and important while finding a relation between two variables is called correlation. In a way, one can say that the concept of correlation is the premise of predictive modelling, in the sense that the correlation is the factor relying on which we say that we can predict outcomes.

A good correlation between two variables suggests that there is a sort of dependence between them. If one is changed, the change will be reflected in the other as well. One can say that a good correlation certifies a mathematical relation between two variables and due to this mathematical relationship, we might be able to predict outcomes. This mathematical relation can be anything. If x and y are two variables, which are correlated, then one can write:

If f is a linear function, then a and b are linearly correlated. If f is an exponential function, then a and b are exponentially correlated:

The degree of correlation between the two variables x and y is quantified...

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