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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

Conventions

In this book, you will find a number of styles of text that distinguish between different kinds of information. Here are some examples of these styles, and an explanation of their meaning.

A typical code snippet would look as follows:

def closest_station(lat, longi):
    stations = np.array([[41.995, -87.933],
                         [41.786, -87.752]])
    loc = np.array([lat, longi])
    deltas = stations - loc[None, :]
    dist2 = (deltas**2).sum(1)
    return np.argmin(dist2)

The outputs of the code snippets are generally shown as the screenshots. This is how a screenshot looks:

Conventions

New terms and important words are shown in bold. Words that you see on the screen, for example, in menus or dialog boxes, appear in the text like this: "The plot of Monthly Income and Monthly Expense for a group of 400 people."

Note

Warnings or important notes appear in a box like this.

Tip

Tips and tricks appear like this.

URLs are shown as below:

https://pypi.python.org/pypi/pip

A lot of tables have been used to summarize the results of mathematical discussions and illustrate certain concepts.

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