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You're reading from  Mastering Predictive Analytics with Python

Product typeBook
Published inAug 2016
Reading LevelIntermediate
Publisher
ISBN-139781785882715
Edition1st Edition
Languages
Python
Concepts
Predictive Analytics
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Author (1)
Joseph Babcock
Joseph Babcock
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Joseph Babcock

Joseph Babcock has spent more than a decade working with big data and AI in the e-commerce, digital streaming, and quantitative finance domains. Through his career he has worked on recommender systems, petabyte scale cloud data pipelines, A/B testing, causal inference, and time series analysis. He completed his PhD studies at Johns Hopkins University, applying machine learning to the field of drug discovery and genomics.
Read more about Joseph Babcock

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Exploring categorical and numerical data in IPython

We will start our explorations in IPython by loading a text file into a DataFrame, calculating some summary statistics, and visualizing distributions. For this exercise we'll use a set of movie ratings and metadata from the Internet Movie Database (http://www.imdb.com/) to investigate what factors might correlate with high ratings for films on this website. Such information might be helpful, for example, in developing a recommendation system based on this kind of user feedback.

Installing IPython notebook

To follow along with the examples, you should have a Windows, Linux, or Mac OSX operating system installed on your computer and access to the Internet. There are a number of options available to install IPython: since each of these resources includes installation guides, we provide a summary of the available sources and direct the reader to the relevant documentation for more in-depth instructions.

  • For most users, a pre-bundled Python environment...

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Mastering Predictive Analytics with Python
Published in: Aug 2016Publisher: ISBN-13: 9781785882715
© 2016 Packt Publishing Limited All Rights Reserved

Author (1)

author image
Joseph Babcock

Joseph Babcock has spent more than a decade working with big data and AI in the e-commerce, digital streaming, and quantitative finance domains. Through his career he has worked on recommender systems, petabyte scale cloud data pipelines, A/B testing, causal inference, and time series analysis. He completed his PhD studies at Johns Hopkins University, applying machine learning to the field of drug discovery and genomics.
Read more about Joseph Babcock

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