Predictive Analytics For Dummies, 2ed
ISBN: 9788126567935
464 pages
For more information write to us at: acadmktg@wiley.com
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.
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