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Part II: Statistical and Machine Learning Algorithms
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Part II: Statistical and Machine Learning Algorithms
by Wee Hyong Tok, Valentine Fontama, Roger Barga
Predictive Analytics with Microsoft Azure Machine Learning, Second Edition
Cover
Title
Copyright
Contents at a Glance
Contents
About the Authors
About the Techincal reviewers
Foreword
Acknowledgments
Introduction
Part I: 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 of Data Mining Technologies
Access to More Data
Faster and Cheaper Processing 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
Introducing the Gallery
Five Easy Steps to Creating a Training Experiment
Step 1: Getting the Data
Step 2: Preprocessing the Data
Step 3: Defining the Features
Step 4: Choosing and Applying Machine Learning Algorithms
Step 5: Predicting Over New Data
Deploying Your Model in Production
Creating a Predictive Experiment
Publishing Your Experiment as a Web Service
Accessing the Azure Machine Learning Web Service
Summary
Chapter 3: Data Preparation
Data Cleaning and Processing
Getting to Know Your Data
Missing and Null Values
Handling Duplicate Records
Identifying and Removing Outliers
Feature Normalization
Dealing with Class Imbalance
Feature Selection
Feature Engineering
Binning Data
The Curse of Dimensionality
Summary
Chapter 4: 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
Chapter 5: Integration with Python
Overview
Python Jumpstart
Using Python in Azure ML Experiments
Using Python for Data Preprocessing
Combining Data using Python
Handling Missing Data Using Python
Feature Selection Using Python
Running Python Code in an Azure ML Experiment
Summary
Part II: Statistical and Machine Learning Algorithms
Chapter 6: 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 III: Practical Applications
Chapter 7: Building Customer Propensity Models
The Business Problem
Data Acquisition and Preparation
Data Analysis
Training the Model
Model Testing and Validation
Model Performance
Prioritizing Evaluation Metrics
Summary
Chapter 8: Visualizing Your Models with Power BI
Overview
Introducing Power BI
Three Approaches for Visualizing with Power BI
Scoring Your Data in Azure Machine Learning and Visualizing in Excel
Scoring and Visualizing Your Data in Excel
Scoring Your Data in Azure Machine Learning and Visualizing in powerbi.com
Loading Data
Building Your Dashboard
Summary
Chapter 9: 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 10: 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 11: Building Predictive Maintenance Models
Overview
Predictive Maintenance Scenarios
The Business Problem
Data Acquisition and Preparation
The Dataset
Data Loading
Data Analysis
Training the Model
Model Testing and Validation
Model Performance
Techniques for Improving the Model
Upsampling and Downsampling
Model Deployment
Creating a Predictive Experiment
Publishing Your Experiment as a Web Service
Summary
Chapter 12: Recommendation Systems
Overview
Recommendation Systems Approaches and Scenarios
The Business Problem
Data Acquisition and Preparation
The Dataset
Training the Model
Model Testing and Validation
Summary
Chapter 13: Consuming and Publishing Models on Azure Marketplace
What Are Machine Learning APIs?
How to Use an API from Azure Marketplace
Publishing Your Own Models in Azure Marketplace
Creating and Publishing a Web Service for Your Machine Learning Model
Creating Scoring Experiment
Publishing Your Experiment as a Web Service
Obtaining the API Key and the Details of the OData Endpoint
Publishing Your Model as an API in Azure Marketplace
Summary
Chapter 14: Cortana Analytics
What Is the Cortana Analytics Suite?
Capabilities of Cortana Analytics Suite
Example Scenario
Summary
Index
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Chapter 5: Integration with Python
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Chapter 6: Introduction to Statistical and Machine Learning Algorithms
PART II
Statistical and Machine Learning Algorithms
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