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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

Generating random numbers and their usage


Random numbers are just like any other number in their property except for the fact that they assume a different value every time the call statement to generate a random number is executed. Random number generating methods use certain algorithms to generate different numbers every time, which are beyond the scope of this book. However, after a finitely large period, they might start generating the already generated numbers. In that sense, these numbers are not truly random and are sometimes called pseudo-random numbers.

In spite of them actually being pseudo-random, these numbers can be assumed to be random for all practical purposes. These numbers are of critical importance to predictive analysts because of the following points:

  • They allow analysts to perform simulations for probabilistic multicase scenarios

  • They can be used to generate dummy data frames or columns of a data frame that are needed in the analysis

  • They can be used for the random sampling...

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