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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

Summary

A logistic regression is a versatile technique used widely in the cases where the variable to be predicted is a binary (or categorical) variable. This chapter dives deep into the math behind the logistics regression and the process to implement it using the scikit-learn and statsmodel api modules. It is important to understand the math behind the algorithm so that the model is not used as a black box without knowing what is going on behind the hood. To recap, the following are the main takeaways from the chapter:

  • Linear regression wouldn't be an appropriate model to predict binary variables as the predictor variables can range from -infinity to +infinity, while the binary variable would be 0 or 1.
  • The odds of a certain event happening is the probability of that event happening divided by the probability of that event not happening. The higher the odds, the higher are the chances of the event happening. The odds can range from 0 to infinity.
  • The final equation for the logistic...
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