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Practical Predictive Analytics

By : Winters

Practical Predictive Analytics

By: Winters

Overview of this book

This is the go-to book for anyone interested in the steps needed to develop predictive analytics solutions with examples from the world of marketing, healthcare, and retail. We'll get started with a brief history of predictive analytics and learn about different roles and functions people play within a predictive analytics project. Then, we will learn about various ways of installing R along with their pros and cons, combined with a step-by-step installation of RStudio, and a description of the best practices for organizing your projects. On completing the installation, we will begin to acquire the skills necessary to input, clean, and prepare your data for modeling. We will learn the six specific steps needed to implement and successfully deploy a predictive model starting from asking the right questions through model development and ending with deploying your predictive model into production. We will learn why collaboration is important and how agile iterative modeling cycles can increase your chances of developing and deploying the best successful model. We will continue your journey in the cloud by extending your skill set by learning about Databricks and SparkR, which allow you to develop predictive models on vast gigabytes of data.

Preface

Preface

What this book covers

What you need for this book

Who this book is for

Conventions

Reader feedback

Customer support

Free Chapter

Getting Started with Predictive Analytics

Getting Started with Predictive Analytics

Predictive analytics are in so many industries

Skills and roles that are important in Predictive Analytics

Predictive analytics software

Other helpful tools

R

How is a predictive analytics project organized?

GUIs

Getting started with RStudio

The R console

The source window

Our first predictive model

Your second script

R packages

References

Summary

The Modeling Process

The Modeling Process

Advantages of a structured approach

Analytic process methodologies

An analytics methodology outline – specific steps

Step 2 data understanding

Step 3 data preparation

Step 4 modeling

Step 5 evaluation

Step 6 deployment

References

Summary

Inputting and Exploring Data

Inputting and Exploring Data

Data input

Joining data

Exploring the hospital dataset

Transposing a dataframe

Missing values

Imputing categorical variables

Outliers

Data transformations

Variable reduction/variable importance

References

Summary

Introduction to Regression Algorithms

Introduction to Regression Algorithms

Supervised versus unsupervised learning models

Regression techniques

Generalized linear models

Logistic regression

Summary

Introduction to Decision Trees, Clustering, and SVM

Introduction to Decision Trees, Clustering, and SVM

Decision tree algorithms

Cluster analysis

Support vector machines

References

Summary

Using Survival Analysis to Predict and Analyze Customer Churn

Using Survival Analysis to Predict and Analyze Customer Churn

What is survival analysis?

Our customer satisfaction dataset

Partitioning into training and test data

Setting the stage by creating survival objects

Examining survival curves

Cox regression modeling

Time-based variables

Comparing the models

Variable selection

Summary

Using Market Basket Analysis as a Recommender Engine

Using Market Basket Analysis as a Recommender Engine

What is market basket analysis?

Examining the groceries transaction file

The sample market basket

Association rule algorithms

Antecedents and descendants

Evaluating the accuracy of a rule

Preparing the raw data file for analysis

Analyzing the input file

Scrubbing and cleaning the data

Removing colors automatically

Filtering out single item transactions

Merging the results back into the original data

Compressing descriptions using camelcase

Creating the test and training datasets

Creating the market basket transaction file

Method two – Creating a physical transactions file

Converting to a document term matrix

K-means clustering of terms

Predicting cluster assignments

Running the apriori algorithm on the clusters

Summarizing the metrics

References

Summary

Exploring Health Care Enrollment Data as a Time Series

Exploring Health Care Enrollment Data as a Time Series

Time series data

Health insurance coverage dataset

Housekeeping

Read the data in

Subsetting the columns

Description of the data

Target time series variable

Saving the data

Determining all of the subset groups

Merging the aggregate data back into the original data

Checking the time intervals

Picking out the top groups in terms of average population size

Plotting the data using lattice

Plotting the data using ggplot

Sending output to an external file

Examining the output

Detecting linear trends

Automating the regressions

Ranking the coefficients

Merging scores back into the original dataframe

Plotting the data with the trend lines

Plotting all the categories on one graph

Performing some automated forecasting using the ets function

Smoothing the data using moving averages

Simple moving average

Verifying the SMA calculation

Exponential moving average

Using the ets function

Forecasting using ALL AGES

Plotting the predicted and actual values

The forecast (fit) method

Plotting future values with confidence bands

Modifying the model to include a trend component

Running the ets function iteratively over all of the categories

Accuracy measures produced by onestep

Comparing the Test and Training for the "UNDER 18 YEARS" group

Accuracy measures

References

Summary

Introduction to Spark Using R

Introduction to Spark Using R

About Spark

Spark environments

SparkR

Building our first Spark dataframe

Importing the sample notebook

Creating a new notebook

Becoming large by starting small

Running the code

Running the initialization code

Extracting the Pima Indians diabetes dataset

Simulating the data

Simulating the negative cases

Running summary statistics

Saving your work

Summary

Exploring Large Datasets Using Spark

Exploring Large Datasets Using Spark

Performing some exploratory analysis on positives

Cleaning up and caching the table in memory

Some useful Spark functions to explore your data

Creating new columns

Constructing a cross-tab

Contrasting histograms

Plotting using ggplot

Spark SQL

Exporting data from Spark back into R

Running local R packages

Some tips for using Spark

Summary

Spark Machine Learning - Regression and Cluster Models

Spark Machine Learning - Regression and Cluster Models

About this chapter/what you will learn

Splitting the data into train and test datasets

Spark machine learning using logistic regression

Running predictions for the test data

Combining the training and test dataset

Exposing the three tables to SQL

Validating the regression results

Calculating goodness of fit measures

Confusion matrix for test group

Plotting outside of Spark

Creating some global views

Normalizing the data

Characterizing the clusters by their mean values

Summary

Spark Models – Rule-Based Learning

Spark Models – Rule-Based Learning

Loading the stop and frisk dataset

Reading the table

Discovering the important features

Running the OneR model

Another OneR example

Constructing a decision tree using Rpart

Running an alternative model in Python

Indexing the classification features

Summary

Splitting the data into train and test datasets

Proceed to create our test and train datasets. The objective will be to sample 80% of the data for the training set and 20% of the data for the test data set.

To speed up sampling somewhat, we can sequentially sample the tails of the sample_bin range for the test dataset and then use the middle for the training data. This is still a random sample, since sample_bin was originally generated randomly and the sequence or range of the numbers have no bearing on the randomness.

Generating the training datasets

Since we want 80% of our data to be training data, first take all of the sample_bin numbers which lie between the high and low cutoff values. We can define the cutoff range as...

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