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by Dursun Delen
Predictive Analytics: Data Mining, Machine Learning and Data Science for Practitioners, 2nd Edition
Cover Page
Title Page
Contents
Table of Contents
Chapter 1. Introduction to Predictive Analytics
What Are the Reasons for the Sudden Popularity of Analytics and Data Science?
The Application Areas of Analytics
The Main Challenges of Analytics
A Longitudinal View of Analytics
A Simple Taxonomy for Analytics
The Cutting Edge of Analytics: IBM Watson
Summary
References
Chapter 2. Introduction to Predictive Analytics and Data Mining
What Is Data Mining?
What Data Mining Is Not
The Most Common Data Mining Applications
What Kinds of Patterns Can Data Mining Discover?
Popular Data Mining Tools
The Dark Side of Data Mining: Privacy Concerns
Application Example: Data Mining for Hollywood Managers
Conclusions
Summary
References
Chapter 3. Standardized Processes for Predictive Analytics
The Knowledge Discovery in Databases (KDD) Process
Cross-Industry Standard Process for Data Mining (CRISP-DM)
SEMMA
SEMMA versus CRISP-DM
Six Sigma for Data Mining
Which Methodology Is Best?
An Exemplary Data Mining Case: Mining Cancer Data for New Knowledge
Summary
References
Chapter 4. Data and Methods for Predictive Analytics
The Nature of Data in Data Analytics
Preprocessing of Data for Analytics
Data Mining Methods
Prediction
Classification
Decision Trees
Cluster Analysis for Data Mining
k-Means Clustering Algorithm
Association
Apriori Algorithm
Data Mining and Predictive Analytics Misconceptions and Realities
Summary
References
Chapter 5. Algorithms for Predictive Analytics
Naïve Bayes
Nearest Neighbor
Similarity Measure: The Distance Metric
Artificial Neural Networks
Support Vector Machines
Linear Regression
Logistic Regression
Time-Series Forecasting
Application Example: Data Mining for Complex Medical Procedures
Summary
References
Chapter 6. Advanced Topics in Predictive Modeling
Model Ensembles
Bias-Variance Tradeoff in Predictive Analyztics
Imbalanced Data Problem in Predictive Analytics
Explainability of Machine Learning Models for Predictive Analytics
Application Case: To Imprison or Not to Imprison—A Predictive Analytics-Based Decision Support System for Drug Courts
Summary
References
Chapter 7. Text Analytics, Topic Modeling, and Sentiment Analysis
Natural Language Processing
Text Mining Applications
The Text Mining Process
Application Example: Text Mining of Research Literature
Text Mining Tools
Topic Modeling
Sentiment Analysis
Application Example: Text-Based Deception Detection
Summary
References
Chapter 8. Big Data for Predictive Analytics
Where Does Big Data Come From?
The Vs That Define Big Data
Fundamental Concepts of Big Data
The Business Problems That Big Data Analytics Addresses
Big Data Technologies
Data Scientists
Big Data and Stream Analytics
Data Stream Mining
Application Example: Big Data for Political Campaigns
Summary
References
Chapter 9. Deep Learning and Cognitive Computing
Introduction to Deep Learning
Basics of “Shallow” Neural Networks
Elements of an Artificial Neural Network
Deep Neural Networks
Convolutional Neural Networks
Recurrent Networks and Long Short-Term Memory Networks
Computer Frameworks for Implementation of Deep Learning
Cognitive Computing
Application Case: Fighting Fraud with Deep Learning
Summary
References
Appendix A. KNIME and the Landscape of Tools for Business Analytics and Data Science
Project Constraints: Time and Money
The Learning Curve
The KNIME Community
Correctness and Flexibility
Extensive Coverage of Data Science Techniques
Data Science in the Enterprise
Summary
References
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