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Practical Predictive Analytics

You're reading from   Practical Predictive Analytics Analyse current and historical data to predict future trends using R, Spark, and more

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Product type Paperback
Published in Jun 2017
Publisher Packt
ISBN-13 9781785886188
Length 576 pages
Edition 1st Edition
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Author (1):
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Ralph Winters Ralph Winters
Author Profile Icon Ralph Winters
Ralph Winters
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Table of Contents (13) Chapters Close

Preface 1. Getting Started with Predictive Analytics FREE CHAPTER 2. The Modeling Process 3. Inputting and Exploring Data 4. Introduction to Regression Algorithms 5. Introduction to Decision Trees, Clustering, and SVM 6. Using Survival Analysis to Predict and Analyze Customer Churn 7. Using Market Basket Analysis as a Recommender Engine 8. Exploring Health Care Enrollment Data as a Time Series 9. Introduction to Spark Using R 10. Exploring Large Datasets Using Spark 11. Spark Machine Learning - Regression and Cluster Models 12. Spark Models – Rule-Based Learning

Loading the stop and frisk dataset

We will be using the diabetes dataset which was constructed in the last chapter. For some of the other decision tree examples, we will need to load the stop and frisk dataset. You can obtain this dataset from the following URL: http://www1.nyc.gov/site/nypd/stats/reports-analysis/stopfrisk.page.

Select the 2015 CSV zip archive and download and extract the files to the projects directory, e.g C:/PracticalPredictiveAnalytics/Data, and name the file 2015_sqf_csv

Importing the CSV file to databricks

Databricks contains a simple user interface which allows you to load a file to the Databricks HDFS filesystem. Alternatively, you can load the file directly to Amazon Web Services (AWS) and read the...

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