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Predictive Analytics: Data Mining, Machine Learning and Data Science for Practitioners, 2nd Edition, 2nd Edition
- By Dursun Delen
- Published Dec 21, 2020 by Pearson FT Press. Part of the Pearson Business Analytics Series series.
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- Includes EPUB and PDF
- About eBook Formats
This eBook includes the following formats, accessible from your Account page after purchase:
EPUB
The open industry format known for its reflowable content and usability on supported mobile devices.
PDF
The popular standard, used most often with the free Acrobat® Reader® software.
This eBook requires no passwords or activation to read. We customize your eBook by discreetly watermarking it with your name, making it uniquely yours.
About
Features
- An end-to-end, holistic guide to theory and practice – packed with conceptual illustrations, example problems and solutions, and case studies
- Presents rich machine learning algorithms, the latest trends and methods, and plenty of hands-on tutorials
- By Dr. Dursun Delen, one of the world’s leading experts in advanced business analytics
Description
- Copyright 2021
- Dimensions: 6" x 9"
- Pages: 448
- Edition: 2nd
- Book
- ISBN-10: 0-13-673851-6
- ISBN-13: 978-0-13-673851-0
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 provides a holistic approach covering 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 is 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
Sample Content
Online Sample Chapter
Introduction to Predictive Analytics and Data Mining
Table of Contents
Foreword
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
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
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?
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
Naive Bayes
Nearest Neighbor
Similarity Measure: The Distance Metric
Artificial Neural Networks
Support Vector Machines
Linear Regression
Logistic Regression
Time-Series Forecasting
Summary
References
Chapter 6 Advanced Topics in Predictive Modeling
Model Ensembles
BiasVariance Trade-off in Predictive Analytics
Imbalanced Data Problems in Predictive Analytics
Explainability of Machine Learning Models for
Predictive Analytics
Summary
References
Chapter 7 Text Analytics, Topic Modeling, and Sentiment Analysis
Natural Language Processing
Text Mining Applications
The Text Mining Process
Text Mining Tools
Topic Modeling
Sentiment Analysis
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
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
Summary
References
Appendix A KNIME and the Landscape of Tools for Business Analytics and Data Science
9780136738510 TOC 11/12/2020
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