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Predictive Analytics For Dummies, 2nd Edition

Book Description

Real-world tips for creating business value

Details on modeling, data clustering, and more

Enterprise use cases to help you get started

Learn to predict the future!

Business today relies on effectively using data to predict trends and sales. Predictive analytics is the tool that can make it happen, and this book eliminates the tricks and shows you how to use it. You'll learn to prepare and process your data, create goals, build a predictive model, get your organization's stakeholders on board, and more.

Inside...

  • How to start a project
  • Identifying data types
  • Modeling tips
  • Working with algorithms
  • How data clustering works
  • How data classification works
  • How deep learning works
  • Advice on presentations
  • Step-by-step predictive modeling

Table of Contents

    1. Cover
    2. Introduction
      1. About This Book
      2. Foolish Assumptions
      3. Icons Used in This Book
      4. Beyond the Book
      5. Where to Go from Here
    3. Part 1: Getting Started with Predictive Analytics
      1. Chapter 1: Entering the Arena
        1. Exploring Predictive Analytics
        2. Adding Business Value
        3. Starting a Predictive Analytic Project
        4. Ongoing Predictive Analytics
        5. Forming Your Predictive Analytics Team
        6. Surveying the Marketplace
      2. Chapter 2: Predictive Analytics in the Wild
        1. Online Marketing and Retail
        2. Implementing a Recommender System
        3. Target Marketing
        4. Personalization
        5. Content and Text Analytics
      3. Chapter 3: Exploring Your Data Types and Associated Techniques
        1. Recognizing Your Data Types
        2. Identifying Data Categories
        3. Generating Predictive Analytics
        4. Connecting to Related Disciplines
      4. Chapter 4: Complexities of Data
        1. Finding Value in Your Data
        2. Constantly Changing Data
        3. Complexities in Searching Your Data
        4. Differentiating Business Intelligence from Big-Data Analytics
        5. Exploration of Raw Data
    4. Part 2: Incorporating Algorithms in Your Models
      1. Chapter 5: Applying Models
        1. Modeling Data
        2. Healthcare Analytics Case Studies
        3. Social and Marketing Analytics Case Studies
        4. Prognostics and its Relation to Predictive Analytics
        5. The Rise of Open Data
      2. Chapter 6: Identifying Similarities in Data
        1. Explaining Data Clustering
        2. Converting Raw Data into a Matrix
        3. Identifying Groups in Your Data
        4. Finding Associations in Data Items
        5. Applying Biologically Inspired Clustering Techniques
      3. Chapter 7: Predicting the Future Using Data Classification
        1. Explaining Data Classification
        2. Introducing Data Classification to Your Business
        3. Exploring the Data-Classification Process
        4. Using Data Classification to Predict the Future
        5. Ensemble Methods to Boost Prediction Accuracy
        6. Deep Learning
    5. Part 3: Developing a Roadmap
      1. Chapter 8: Convincing Your Management to Adopt Predictive Analytics
        1. Making the Business Case
        2. Gathering Support from Stakeholders
        3. Presenting Your Proposal
      2. Chapter 9: Preparing Data
        1. Listing the Business Objectives
        2. Processing Your Data
        3. Working with Features
        4. Structuring Your Data
      3. Chapter 10: Building a Predictive Model
        1. Getting Started
        2. Developing and Testing the Model
        3. Going Live with the Model
      4. Chapter 11: Visualization of Analytical Results
        1. Visualization as a Predictive Tool
        2. Evaluating Your Visualization
        3. Visualizing Your Model’s Analytical Results
        4. Novel Visualization in Predictive Analytics
        5. Big Data Visualization Tools
    6. Part 4: Programming Predictive Analytics
      1. Chapter 12: Creating Basic Prediction Examples
        1. Installing the Software Packages
        2. Preparing the Data
        3. Making Predictions Using Classification Algorithms
      2. Chapter 13: Creating Basic Examples of Unsupervised Predictions
        1. Getting the Sample Dataset
        2. Using Clustering Algorithms to Make Predictions
      3. Chapter 14: Predictive Modeling with R
        1. Programming in R
        2. Making Predictions Using R
      4. Chapter 15: Avoiding Analysis Traps
        1. Data Challenges
        2. Analysis Challenges
    7. Part 5: Executing Big Data
      1. Chapter 16: Targeting Big Data
        1. Major Technological Trends in Predictive Analytics
        2. Applying Open-Source Tools to Big Data
      2. Chapter 17: Getting Ready for Enterprise Analytics
        1. Analytics as a Service
        2. Preparing for a Proof-of-Value of Predictive Analytics Prototype
    8. Part 6: The Part of Tens
      1. Chapter 18: Ten Reasons to Implement Predictive Analytics
        1. Identifying 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. Gaining Rapid Access to Information
        8. Making Informed Decisions
        9. Gaining Competitive Edge
        10. Improving the Business
      2. Chapter 19: Ten Steps to Build a Predictive Analytic Model
        1. Building a Predictive Analytics Team
        2. Setting the Business Objectives
        3. Preparing Your Data
        4. Sampling Your Data
        5. Avoiding “Garbage In, Garbage Out”
        6. Creating Quick Victories
        7. Fostering Change in Your Organization
        8. Building Deployable Models
        9. Evaluating Your Model
        10. Updating Your Model
    9. About the Authors
    10. Connect with Dummies
    11. End User License Agreement