Predictive Analytics (2nd Ed.) Data Mining, Machine Learning and Data Science for Practitioners Pearson Business Analytics Series
Auteur : Delen Dursun
* 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
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
Bias–Variance 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
- 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
Date de parution : 01-2021
Ouvrage de 448 p.
10x10 cm
Disponible chez l'éditeur (délai d'approvisionnement : 14 jours).
Prix indicatif 49,42 €
Ajouter au panierThèmes de Predictive Analytics :
Mots-clés :
predictive analytics; business analytics; optimization + predictive analytics; predictive analytics + data mining; data analysis; data exploration; data modeling; applied statistics; statistical research; operations research; quantitative methods; management science; data science; decision science; decision modeling