Predictive analytics for dummies

Introduction



Part 1: Getting Started with Predictive Analytics

Chapter 1: Entering the Arena

Exploring Predictive Analytics
Mining data
Highlighting the model
Adding Business Value
Endless opportunities
Empowering your organization
Starting a Predictive Analytic Project
Business knowledge
Data-science team and technology
The Data
Ongoing Predictive Analytics
Forming Your Predictive Analytics Team
Hiring experienced practitioners
Demonstrating commitment and curiosity
Surveying the Marketplace
Responding to big data
Working with big data


Chapter 2: Predictive Analytics in the Wild

Online Marketing and Retail
Recommender systems
Personalized shopping on the Internet
Implementing a Recommender System
Collaborative filtering
Content-based filtering
Hybrid recommender systems
Target Marketing
Targeting using predictive modeling
Uplift modeling
Personalization
Online customer experience
Retargeting
Implementation
Optimizing using personalization
Similarities of Personalization and Recommendations
Content and Text Analytics


Chapter 3: Exploring Your Data Types and Associated Techniques

Recognizing Your Data Types
Structured and unstructured data
Static and streamed data
Identifying Data Categories
Attitudinal data
Behavioral data
Demographic data
Generating Predictive Analytics
Data-driven analytics
User-driven analytics
Connecting to Related Disciplines
Statistics
Data mining
Machine learning


Chapter 4: Complexities of Data

Finding Value in Your Data
Delving into your data
Data validity
Data variety
Constantly Changing Data
Data velocity
High volume of data
Complexities in Searching Your Data
Keyword-based search
Semantic-based search
Contextual search
Differentiating Business Intelligence from Big-Data Analytics
Exploration of Raw Data
Identifying data attributes
Exploring common data visualizations
Tabular visualizations
Word clouds
Flocking birds as a novel data representation
Graph charts
Common visualizations


Part 2: Incorporating Algorithms in Your Models

Chapter 5: Applying Models

Modeling Data
Models and simulation
Categorizing models
Describing and summarizing data
Making better business decisions
Healthcare Analytics Case Studies
Google Flu Trends
Cancer survivability predictors
Social and Marketing Analytics Case Studies
Target store predicts pregnant women
Twitter-based predictors of earthquakes
Twitter-based predictors of political campaign outcomes
Tweets as predictors for the stock market
Predicting variation of stock prices from news articles
Analyzing New York City's bicycle usage
Predictions and responses
Data compression
Prognostics and its Relation to Predictive Analytics
The Rise of Open Data


Chapter 6: Identifying Similarities in Data

Explaining Data Clustering
Converting Raw Data into a Matrix
Creating a matrix of terms in documents
Term selection
Identifying Groups in Your Data
K-means clustering algorithm
Clustering by nearest neighbors
Density-based algorithms
Finding Associations in Data Items
Applying Biologically Inspired Clustering Techniques
Birds flocking: Flock by Leader algorithm
Ant colonies


Chapter 7: Predicting the Future Using Data Classification

Explaining Data Classification
Introducing Data Classification to Your Business
Exploring the Data-Classification Process
Using Data Classification to Predict the Future
Decision trees
Algorithms for Generating Decision Trees
Support vector machine
Ensemble Methods to Boost Prediction Accuracy
Naïve Bayes classification algorithm
The Markov Model
Linear regression
Neural networks
Deep Learning


Part 3: Developing A Roadmap

Chapter 8: Convincing Your Management to Adopt Predictive Analytics

Making the Business Case
Gathering Support from Stakeholders
Presenting Your Proposal


Chapter 9: Preparing Data

Listing the Business Objectives
Processing Your Data
Identifying the data
Cleaning the data
Generating any derived data
Reducing the dimensionality of your data
Applying principal component analysis
Leveraging singular value decomposition
Working with Features
Structuring Your Data
Extracting, transforming and loading your data
Keeping the data up to date
Outlining testing and test data


Chapter 10: Building a Predictive Model

Getting Started
Defining your business objectives
Preparing your data
Choosing an algorithm
Developing and Testing the Model
Going Live with the Model


Chapter 11: Visualization of Analytical Results

Visualization as a Predictive Tool
Evaluating Your Visualization
Visualizing Your Model's Analytical Results
Visualizing hidden groupings in your data
Visualizing data classification results
Visualizing outliers in your data
Visualization of Decision Trees
Visualizing predictions
Novel Visualization in Predictive Analytics
Big Data Visualization Tools
Tableau
Google Charts
Plotly
Infogram


Part 4: Programming Predictive Analytics

Chapter 12: Creating Basic Prediction Examples

Installing the Software Packages
Installing Python
Installing the machine-learning module
Installing the dependencies
Preparing the Data
Making Predictions Using Classification Algorithms
Creating a supervised learning model with SVM
Creating a supervised learning model with logistic regression
Creating a supervised learning model with random forest
Comparing the classification models


Chapter 13: Creating Basic Examples of Unsupervised Predictions

Getting the Sample Dataset
Using Clustering Algorithms to Make Predictions
Comparing clustering models
Creating an unsupervised learning model with K-means
Creating an unsupervised learning model with DBSCAN
Creating an unsupervised learning model with mean shift


Chapter 14: Predictive Modeling with R

Programming in R
Installing R
Installing RStudio
Getting familiar with the environment
Learning just a bit of R
Making Predictions Using R
Predicting using regression
Using classification to predict
Classification by random forest


Chapter 15: Avoiding Analysis Traps

Data Challenges
Outlining the limitations of the data
Dealing with extreme cases (outliers)
Data smoothing
Curve fitting
Keeping the assumptions to a minimum
Analysis Challenges


Part 5: Executing Big Data

Chapter 16: Targeting Big Data

Major Technological Trends in Predictive Analytics
Exploring predictive analytics as a service
Aggregating distributed data for analysis
Real-time data-driven analytics
Applying Open-Source Tools to Big Data
Apache Hadoop
Apache Spark


Chapter 17: Getting Ready for Enterprise Analytics

Analytics as a Service
Google Analytics
IBM Watson
Microsoft Revolution R Enterprise
Preparing for a Proof-of-Value of Predictive Analytics Prototype
Prototyping for predictive analytics
Testing your predictive analytics model


Part 6: The Part of Tens

Chapter 18: Ten Reasons to Implement

Predictive Analytics


Chapter 19: Ten Steps to Build a Predictive Analytic Model



Index

Description
Predictive Analytics For Dummies, 2e will help the you understand the core of predictive analytics and get started putting it to use with readily available tools to collect and analyze data. You will learn how to incorporate algorithms through discovering data models, identifying similarities and relationships in your data, and how to predict the future through data classification. You will develop a roadmap by preparing your data, creating goals, processing your data, and building a predictive model that will get stakeholder buy-in. The author will also address "soft" issues, including handling people, setting realistic goals, protecting budgets, making useful presentations, and more, to help the reader prepare for shepherding predictive analysis projects through their companies.