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The Unsupervised Learning Workshop

You're reading from   The Unsupervised Learning Workshop Get started with unsupervised learning algorithms and simplify your unorganized data to help make future predictions

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Product type Paperback
Published in Jul 2020
Publisher Packt
ISBN-13 9781800200708
Length 550 pages
Edition 1st Edition
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Authors (3):
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Benjamin Johnston Benjamin Johnston
Author Profile Icon Benjamin Johnston
Benjamin Johnston
Christopher Kruger Christopher Kruger
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Christopher Kruger
Aaron Jones Aaron Jones
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Aaron Jones
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Toc

Table of Contents (11) Chapters Close

Preface
1. Introduction to Clustering 2. Hierarchical Clustering FREE CHAPTER 3. Neighborhood Approaches and DBSCAN 4. Dimensionality Reduction Techniques and PCA 5. Autoencoders 6. t-Distributed Stochastic Neighbor Embedding 7. Topic Modeling 8. Market Basket Analysis 9. Hotspot Analysis Appendix

Cleaning Text Data

A key component of all successful modeling exercises is a clean dataset that has been appropriately and sufficiently preprocessed for the specific data type and analysis being performed. Text data is no exception, as it is virtually unusable in its raw form. It does not matter what algorithm is being run: if the data isn't properly prepared, the results will be at best meaningless and at worst misleading. As the saying goes, garbage in, garbage out. For topic modeling, the goal of data cleaning is to isolate the words in each document that could be relevant by removing everything that could be obstructive.

Data cleaning and preprocessing is almost always specific to the dataset, meaning that each dataset will require a unique set of cleaning and preprocessing steps selected to specifically handle the issues in it. With text data, cleaning and preprocessing steps can include language filtering, removing URLs and screen names, lemmatizing, and stop word removal...

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