Predictive Analytics For Dummies

Table of contents

1. 1. Introduction
      1. About This Book
      2. Foolish Assumptions
      3. Icons Used in This Book
      4. Beyond the Book
      5. Where to Go from Here
   2. Part I: Getting Started with Predictive Analytics
      1. Chapter 1: Entering the Arena
         1. Exploring Predictive Analytics
            1. Mining data
            2. Highlighting the model
         2. Adding Business Value
            1. Endless opportunities
            2. Empowering your organization
         3. Starting a Predictive Analytic Project
            1. Business knowledge
            2. Data-science team and technology
            3. The Data
         4. Surveying the Marketplace
            1. Responding to big data
            2. Working with big data
      2. Chapter 2: Predictive Analytics in the Wild
         1. Online Marketing and Retail
            1. Recommender systems
         2. Implementing a Recommender System
            1. Collaborative filtering
            2. Content-based filtering
            3. Hybrid recommender systems
         3. Target Marketing
            1. Targeting using predictive modeling
            2. Uplift modeling
         4. Predictive Analytics Fight Fraud and Crime
         5. Content and Text Analytics
      3. Chapter 3: Exploring Your Data Types and Associated Techniques
         1. Recognizing Your Data Types
            1. Structured and unstructured data
            2. Static and streamed data
         2. Identifying Data Categories
            1. Attitudinal data
            2. Behavioral data
            3. Demographic data
         3. Generating Predictive Analytics
            1. Data-driven analytics
            2. User-driven analytics
         4. Connecting to Related Disciplines
            1. Statistics
            2. Data mining
            3. Machine learning
      4. Chapter 4: Complexities of Data
         1. Finding Value in Your Data
            1. Delving into your data
            2. Data validity
            3. Data variety
         2. Constantly Changing Data
            1. Data velocity
            2. High volume of data
         3. Complexities in Searching Your Data
            1. Keyword-based search
            2. Semantic-based search
         4. Differentiating Business Intelligence from Big-Data Analytics
         5. Visualization of Raw Data
            1. Identifying data attributes
            2. Exploring data visualization
            3. Tabular visualizations
            4. Bar charts
            5. Pie charts
            6. Graph charts
            7. Word clouds as representations
            8. Line graphs
            9. Flocking birds representation
   3. Part II: Incorporating Algorithms in Your Models
      1. Chapter 5: Applying Models
         1. Modeling Data
            1. Models and simulation
            2. Categorizing models
            3. Describing and summarizing data
            4. Making better business decisions
         2. Healthcare Analytics Case Studies
            1. Google search queries as epidemic predictors
            2. Cancer survivability predictors
         3. Social and Marketing Analytics Case Studies
            1. Tweets as predictors for the stock market
            2. Target store predicts pregnant women
            3. Twitter-based predictors of earthquakes
            4. Twitter-based predictors of political campaign outcomes
      2. Chapter 6: Identifying Similarities in Data
         1. Explaining Data Clustering
            1. Motivation
         2. Converting Raw Data into a Matrix
            1. Creating a matrix of terms in documents
            2. Term selection
         3. Identifying K-Groups in Your Data
            1. K-means clustering algorithm
            2. Clustering by nearest neighbors
         4. Finding Associations Among Data Items
         5. Applying Biologically Inspired Clustering Techniques
            1. Birds flocking
            2. Ant colonies
      3. Chapter 7: Predicting the Future Using Data Classification
         1. Explaining Data Classification
            1. Lending
            2. Marketing
            3. Healthcare
            4. What’s next?
         2. Introducing Data Classification to Your Business
         3. Exploring the Data-Classification Process
         4. Using Data Classification to Predict the Future
            1. Decision trees
            2. Support vector machine
            3. Naïve Bayes classification algorithm
            4. Neural networks
            5. The Markov Model
            6. Linear regression
         5. Ensemble Methods to Boost Prediction Accuracy
   4. Part III: Developing a Roadmap
      1. Chapter 8: Convincing Your Management to Adopt Predictive Analytics
         1. Making the Business Case
            1. Benefits to the business
         2. Gathering Support from Stakeholders
            1. Working with your sponsors
            2. Getting business and operations buy-in
            3. Getting IT buy-in
            4. Rapid prototyping
         3. Presenting Your Proposal
      2. Chapter 9: Preparing Data
         1. Listing the Business Objectives
            1. Identifying related objectives
            2. Collecting user requirements
         2. Processing Your Data
            1. Identifying the data
            2. Cleaning the data
            3. Generating any derived data
            4. Reducing the dimensionality of your data
         3. Structuring Your Data
            1. Extracting, transforming and loading your data
            2. Keeping the data up to date
            3. Outlining testing and test data
      3. Chapter 10: Building a Predictive Model
         1. Getting Started
            1. Defining your business objectives
            2. Preparing your data
            3. Choosing an algorithm
         2. Developing and Testing the Model
            1. Developing the model
            2. Testing the model
            3. Evaluating the model
         3. Going Live with the Model
            1. Deploying the model
            2. Monitoring and maintaining the model
      4. Chapter 11: Visualization of Analytical Results
         1. Visualization As a Predictive Tool
            1. Why visualization matters
            2. Getting the benefits of visualization
            3. Dealing with complexities
         2. Evaluating Your Visualization
            1. How relevant is this picture?
            2. How interpretable is the picture?
            3. Is the picture simple enough?
            4. Does the picture lead to new insights?
         3. Visualizing Your Model’s Analytical Results
            1. Visualizing hidden groupings in your data
            2. Visualizing data classification results
            3. Visualizing outliers in your data
            4. Visualization of Decision Trees
            5. Visualizing predictions
         4. Other Types of Visualizations in Predictive Analytics
            1. Bird-flocking behavior data visualization
   5. Part IV: Programming Predictive Analytics
      1. Chapter 12: Creating Basic Prediction Examples
         1. Installing the Software Packages
            1. Installing Python
            2. Installing the machine-learning module
            3. Installing the dependencies
         2. Preparing the Data
            1. Getting the sample dataset
            2. Labeling your data
         3. Making Predictions Using Classification Algorithms
            1. Creating a supervised learning model with SVM
            2. Creating a supervised learning model with logistic regression
            3. Comparing two classification models
      2. Chapter 13: Creating Basic Examples of Unsupervised Predictions
         1. Getting the Sample Dataset
         2. Using Clustering Algorithms to Make Predictions
            1. Comparing two clustering models
            2. Creating an unsupervised learning model with K-means
            3. Creating an unsupervised learning model with DBSCAN
      3. Chapter 14: Predictive Modeling with R
         1. Programming in R
            1. Installing R
            2. Installing RStudio
            3. Getting familiar with the environment
            4. Learning just a bit of R
         2. Making Predictions Using R
            1. Predicting using regression
            2. Using classification to predict
      4. Chapter 15: Avoiding Analysis Traps
         1. Data Challenges
            1. Outlining the limitations of the data
            2. Dealing with extreme cases (outliers)
            3. Data smoothing
            4. Curve fitting
            5. Keeping the assumptions to a minimum
         2. Analysis Challenges
            1. Supervised analytics
            2. Relying on only one analysis
            3. Describing the limitations of the model
            4. Avoiding non-scalable models
            5. Scoring your predictions accurately
      5. Chapter 16: Targeting Big Data
         1. Major Technological Trends in Predictive Analytics
            1. Exploring predictive analytics as a service
            2. Aggregating distributed data for analysis
            3. Real-time data-driven analytics
         2. Applying Open-Source Tools to Big Data
            1. Apache Hadoop
            2. Apache Mahout
         3. Building a Rapid Prototype of Your Predictive Analytics Model
            1. Prototyping for predictive analytics
            2. Testing your predictive analytics model
   6. Part V: The Part of Tens
      1. Chapter 17: Ten Reasons to Implement Predictive Analytics
         1. Outlining Business Goals
         2. Knowing Your Data
         3. Organizing Your Data
         4. Satisfying Your Customers
         5. Reducing Operational Costs
         6. Increasing Returns on Investments (ROI)
         7. Increasing Confidence
         8. Making Informed Decisions
         9. Gaining Competitive Edge
         10. Improving the Business
      2. Chapter 18: Ten Steps to Build a Predictive Analytic Model
         1. Building a Predictive Analytics Team
            1. Getting business expertise on board
            2. Firing up IT and math expertise
         2. Setting the Business Objectives
         3. Preparing Your Data
         4. Sampling Your Data
         5. Avoiding “Garbage In, Garbage Out”
            1. Keeping it simple isn’t stupid
            2. Data preparation puts the good stuff in
         6. Creating Quick Victories
         7. Fostering Change in Your Organization
         8. Building Deployable Models
         9. Evaluating Your Model
         10. Updating Your Model
   7. About the Authors
   8. Cheat Sheet
   9. More Dummies Products