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Use Predictive Analytics to Uncover Hidden Patterns and Correlations and Improve Decision-Making
Using predictive analytics techniques, decision-makers can uncover hidden patterns and correlations in their data and leverage these insights to improve many key business decisions. In this thoroughly updated guide, Dr. Dursun Delen illuminates state-of-the-art best practices for predictive analytics for both business professionals and students.
Delen's holistic approach covers key data mining processes and methods, relevant data management techniques, tools and metrics, advanced text and web mining, big data integration, and much more. Balancing theory and practice, Delen presents intuitive conceptual illustrations, realistic example problems, and real-world case studiesincluding lessons from failed projects. It's all designed to help you gain a practical understanding you can apply for profit.
* Leverage knowledge extracted via data mining to make smarter decisions
* Use standardized processes and workflows to make more trustworthy predictions
* Predict discrete outcomes (via classification), numeric values (via regression), and changes over time (via time-series forecasting)
* Understand predictive algorithms drawn from traditional statistics and advanced machine learning
* Discover cutting-edge techniques, and explore advanced applications ranging from sentiment analysis to fraud detection
Chapter 1 Introduction to Analytics
What's in a Name?
Why 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
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
Chapter 3 Standardized Processes for Predictive Analytics
The Knowledge Discovery in Databases (KDD) Process
Cross-Industry Standard Process for Data Mining (CRISP-DM)
SEMMA Versus CRISP-DM
Six Sigma for Data Mining
Which Methodology Is Best?
Chapter 4 Data and Methods for Predictive Analytics
The Nature of Data in Data Analytics
Preprocessing of Data for Analytics
Data Mining Methods
Cluster Analysis for Data Mining
k-Means Clustering Algorithm
Data Mining and Predictive Analytics Misconceptions and Realities
Chapter 5 Algorithms for Predictive Analytics
Similarity Measure: The Distance Metric
Artificial Neural Networks
Support Vector Machines
Chapter 6 Advanced Topics in Predictive Modeling
BiasVariance Trade-off in Predictive Analytics
Imbalanced Data Problems in Predictive Analytics
Explainability of Machine Learning Models for
Chapter 7 Text Analytics, Topic Modeling, and Sentiment Analysis
Natural Language Processing
Text Mining Applications
The Text Mining Process
Text Mining Tools
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
Big Data Technologies
Big Data and Stream Analytics
Data Stream Mining
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
Appendix A KNIME and the Landscape of Tools for Business Analytics and Data Science
9780136738510 TOC 11/12/2020