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Hands-On Data Science and Python Machine Learning

You're reading from   Hands-On Data Science and Python Machine Learning Perform data mining and machine learning efficiently using Python and Spark

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Product type Paperback
Published in Jul 2017
Publisher Packt
ISBN-13 9781787280748
Length 420 pages
Edition 1st Edition
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Author (1):
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Frank Kane Frank Kane
Author Profile Icon Frank Kane
Frank Kane
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Table of Contents (11) Chapters Close

Preface 1. Getting Started FREE CHAPTER 2. Statistics and Probability Refresher, and Python Practice 3. Matplotlib and Advanced Probability Concepts 4. Predictive Models 5. Machine Learning with Python 6. Recommender Systems 7. More Data Mining and Machine Learning Techniques 8. Dealing with Real-World Data 9. Apache Spark - Machine Learning on Big Data 10. Testing and Experimental Design

Data cleaning and normalisation

Now, this is one of the simplest, but yet it might be the most important section in this whole book. We're going to talk about cleaning your input data, which you're going to spend a lot of your time doing.

How well you clean your input data and understand your raw input data is going to have a huge impact on the quality of your results - maybe even more so than what model you choose or how well you tune your models. So, pay attention; this is important stuff!

Cleaning your raw input data is often the most important, and time-consuming, part of your job as a data scientist!

Let's talk about an inconvenient truth of data science, and that's that you spend most of your time actually just cleaning and preparing your data, and actually relatively little of it analyzing it and trying out new algorithms. It's not quite as glamorous...

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