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Learning Data Mining with Python

You're reading from Learning Data Mining with Python Use Python to manipulate data and build predictive models

Product type Paperback

Published in Apr 2017

Publisher Packt

ISBN-13 9781787126787

Length 358 pages

Edition 2nd Edition

Languages

Python

Concepts

Data Mining

Table of Contents (14) Chapters

Preface

1. Getting Started with Data Mining FREE CHAPTER

2. Classifying with scikit-learn Estimators

3. Predicting Sports Winners with Decision Trees

4. Recommending Movies Using Affinity Analysis

5. Features and scikit-learn Transformers

6. Social Media Insight using Naive Bayes

7. Follow Recommendations Using Graph Mining

8. Beating CAPTCHAs with Neural Networks

9. Authorship Attribution

10. Clustering News Articles

11. Object Detection in Images using Deep Neural Networks

12. Working with Big Data

13. Next Steps...

Downloading data from a social network

We are first going to download a corpus of data from Twitter and use it to sort out spam from useful content. Twitter provides a robust API for collecting information from its servers and this API is free for small-scale usage. It is, however, subject to some conditions that you'll need to be aware of if you start using Twitter's data in a commercial setting.

First, you'll need to sign up for a Twitter account (which is free). Go to http://twitter.com and register an account if you do not already have one.

Next, you'll need to ensure that you only make a certain number of requests per minute. This limit is currently 15 requests per 15 minutes (it depends on the exact API). It can be tricky ensuring that you don't breach this limit, so it is highly recommended that you use a library to talk to Twitter's API.

Note

If you are using your own code (that is making the web calls with your own code) to connect with a web-based API, ensure that you read the documentation...

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