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Part 3: Practical Applications
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Part 3: Practical Applications
by Wee Hyong Tok, Valentine Fontama, Roger Barga
Predictive Analytics with Microsoft Azure Machine Learning: Build and Deploy Actionable Solutions in Minutes
Cover
Title
Copyright
Contents at a Glance
Contents
About the Authors
Acknowledgments
Foreword
Introduction
Part 1: Introducing Data Science and Microsoft Azure Machine Learning
Chapter 1: Introduction to Data Science
What Is Data Science?
Analytics Spectrum
Descriptive Analysis
Diagnostic Analysis
Predictive Analysis
Prescriptive Analysis
Why Does It Matter and Why Now?
Data as a Competitive Asset
Increased Customer Demand
Increased Awareness ofData Mining Technologies
Access to More Data
Faster and CheaperProcessing Power
The Data Science Process
Common Data Science Techniques
Classification Algorithms
Clustering Algorithms
Regression Algorithms
Simulation
Content Analysis
Recommendation Engines
Cutting Edge of Data Science
The Rise of Ensemble Models
Summary
Bibliography
Chapter 2: Introducing Microsoft Azure Machine Learning
Hello, Machine Learning Studio!
Components of an Experiment
Five Easy Steps to Creating an Experiment
Step 1: Get Data
Step 2: Preprocess Data
Step 3: Define Features
Step 4: Choose and Apply Machine Learning Algorithms
Step 5: Predict Over New Data
Deploying Your Model in Production
Deploying Your Model into Staging
Testing the Web Service
Moving Your Model from Staging into Production
Accessing the Azure Machine Learning Web Service
Summary
Chapter 3: Integration with R
R in a Nutshell
Building and Deploying Your First R Script
Using R for Data Preprocessing
Using a Script Bundle (Zip)
Building and Deploying a Decision Tree Using R
Summary
Part 2: Statistical and Machine Learning Algorithms
Chapter 4: Introduction to Statistical and Machine Learning Algorithms
Regression Algorithms
Linear Regression
Neural Networks
Decision Trees
Boosted Decision Trees
Classification Algorithms
Support Vector Machines
Bayes Point Machines
Clustering Algorithms
Summary
Part 3: Practical Applications
Chapter 5: Building Customer Propensity Models
The Business Problem
Data Acquisition and Preparation
Loading Data from Your Local File System
Loading Data from Other Sources
Data Analysis
Training the Model
Model Testing and Validation
Model Performance
Summary
Chapter 6: Building Churn Models
Churn Models in a Nutshell
Building and Deploying a Customer Churn Model
Preparing and Understanding Data
Data Preprocessing and Feature Selection
Classification Model for Predicting Churn
Evaluating the Performance of the Customer Churn Models
Summary
Chapter 7: Customer Segmentation Models
Customer Segmentation Models in a Nutshell
Building and Deploying Your First K-Means Clustering Model
Feature Hashing
Identifying the Right Features
Properties of K-Means Clustering
Customer Segmentation of Wholesale Customers
Loading the Data from the UCI Machine Learning Repository
Using K-Means Clustering for Wholesale Customer Segmentation
Cluster Assignment for New Data
Summary
Chapter 8: Building Predictive Maintenance Models
Overview
The Business Problem
Data Acquisition and Preparation
The Dataset
Data Loading
Data Analysis
Training the Model
Model Testing and Validation
Model Performance
Model Deployment
Publishing Your Model into Staging
Moving Your Model from Staging into Production
Summary
Index
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Chapter 4: Introduction to Statistical and Machine Learning Algorithms
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Chapter 5: Building Customer Propensity Models
PART 3
Practical Applications
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