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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

What you need for this book

In order to make the best use of this book, you will require the following:

  • All the datasets that have been used to illustrate the concepts in various chapters. These datasets can be downloaded from this URL: https://goo.gl/zjS4C6. There is a sub-folder containing required datasets for each chapter.
  • Your computer should have any of the Python distribution installed. The examples in the book have been worked upon in IPython Notebook. Following the examples will be much easier if you use IPython Notebook. This comes with Anaconda distribution that can be installed from https://www.continuum.io/downloads.
  • The Python packages which are used widely, for example, pandas, matplotlib, scikit-learn, NumPy, and so on, should be installed. If you install Anaconda these packages will come pre-installed.
  • One of the best ways to use this book will be to take the dataset used to illustrate concepts and flow along with the chapter. The concepts will be easier to understand if the reader works hands on on the examples.
  • A basic aptitude for mathematics is expected. It is beneficial to understand the mathematics behind the algorithms before applying them.
  • Prior experience or knowledge of coding will be an added advantage. But, not a pre-requisite at all.
  • Similarly, knowledge of statistics and some algorithms will be beneficial, but is not a pre-requisite.
  • An open mind curious to learn the tips and tricks of a subject that is going to be an indispensable skillset in the coming future.
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