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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

Exploring the hospital dataset


Exploratory data analysis is a preliminary step prior to data modeling in which you look at all of the characteristics of data in order t0 get a sense of data distribution, correlation, missing values, outliers, and any other factors that might impact future analyses. It is a very important step, and if performed diligently, will save you a lot of time later on.

For the following examples, we will read the NYC hospital discharges dataset (hospital inpatient discharges (SPARCS De-Identified): 2012, n.d.). This example uses the read.csv function to input the delimited file, and then uses the View function to graphically display the output. Then the str function is used to display the contents of the df dataframe that was just created, and then finally, the summary() function displays all of the relevant statistics on all of the variables. These are all typical first steps to perform when looking at data for the first time:

df <-read.csv("C:/PracticalPredictiveAnalytics...
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