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Beginning Data Science with Python and Jupyter

You're reading from   Beginning Data Science with Python and Jupyter Use powerful industry-standard tools within Jupyter and the Python ecosystem to unlock new, actionable insights from your data

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Product type Paperback
Published in Jun 2018
Publisher
ISBN-13 9781789532029
Length 194 pages
Edition 1st Edition
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Author (1):
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Alex Galea Alex Galea
Author Profile Icon Alex Galea
Alex Galea
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Chapter 2. Data Cleaning and Advanced Machine Learning

The goal of data analytics in general is to uncover actionable insights that result in positive business outcomes. In the case of predictive analytics, the aim is to do this by determining the most likely future outcome of a target, based on previous trends and patterns.

The benefits of predictive analytics are not restricted to big technology companies. Any business can find ways to benefit from machine learning, given the right data.

Companies all around the world are collecting massive amounts of data and using predictive analytics to cut costs and increase profits. Some of the most prevalent examples of this are from the technology giants Google, Facebook, and Amazon, who utilize big data on a huge scale. For example, Google and Facebook serve you personalized ads based on predictive algorithms that guess what you are most likely to click on. Similarly, Amazon recommends personalized products that you are most likely to...

You have been reading a chapter from
Beginning Data Science with Python and Jupyter
Published in: Jun 2018
Publisher:
ISBN-13: 9781789532029
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