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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 algorithms

The choice of which algorithm to deploy to answer a business question depends on a variety of parameters, and there is no one good answer. The choice of algorithm generally depends on the nature of the predictor and output variables; also, the overarching nature of the business problem at hand—whether it is a numerical prediction, classification, or an aggregation problem. Based on these preliminary criteria, one can shortlist a few existing methods to apply on the dataset.

Each method will have its own pros and cons, and the final decision should be taken keeping in mind the business context. The decision for the best-suited algorithm is usually taken based on the following two requirements:

  • Sometimes, the user of the result is interested only in the accuracy of the results. In such cases, the choice of the algorithm is done based on the accuracy of the algorithms. All the qualifying models are run and the one with the maximum accuracy is finalized.
  • At...
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