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Mastering Predictive Analytics with Python

You're reading from Mastering Predictive Analytics with Python Exploit the power of data in your business by building advanced predictive modeling applications with Python

Product type Paperback

Published in Aug 2016

Publisher

ISBN-13 9781785882715

Length 334 pages

Edition 1st Edition

Languages

Python

Concepts

Predictive Analytics

Author (1):

Joseph Babcock

View More author details

Table of Contents (11) Chapters

Preface

1. From Data to Decisions – Getting Started with Analytic Applications FREE CHAPTER

2. Exploratory Data Analysis and Visualization in Python

3. Finding Patterns in the Noise – Clustering and Unsupervised Learning

4. Connecting the Dots with Models – Regression Methods

5. Putting Data in its Place – Classification Methods and Analysis

6. Words and Pixels – Working with Unstructured Data

7. Learning from the Bottom Up – Deep Networks and Unsupervised Features

8. Sharing Models with Prediction Services

9. Reporting and Testing – Iterating on Analytic Systems

Index

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...

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Authors (1)

Joseph Babcock

Joseph Babcock

Joseph Babcock has spent over a decade working with big data and AI in the e-commerce, digital streaming, and quantitative finance domains. Throughout 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 drug discovery and genomics.

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