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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

Best practices for business contexts

This is the meatiest part of the report created for a predictive modeling project. Some users of the report will navigate directly to this section as they are primarily interested in the overall effect of the project. Thus, it is imperative to mention the highlights and most important findings of the project in this section. This is different from reporting the statistics, which is in a way the raw output of the predictive model. In this section, we will focus on the following:

  • Findings and insights of the analyses
  • Major problems identified
  • Major results from the model
  • The accuracy or efficiency of the model
  • Action steps for the user to solve the business problem, and so on

If it is a customer segmentation problem, mention the names and characteristics of the segments identified along with the statistical summary for each segment. Recommend a plan to maximize sales and revenue (or whatever the business objective might be) for each of the segments.

If it is...

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