PREDICTIVE MODELS TO RISK ANALYSIS WITH NEURAL NETWORKS. REGRESSION AND DECISION TREES
PREDICTIVE MODELS TO RISK ANALYSIS WITH NEURAL NETWORKS. REGRESSION AND DECISION TREES

Author:

Publisher: CESAR PEREZ

Published:

Total Pages: 222

ISBN-13: 100897952X

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The essential aim of this book is to use predictive models to analyze risk. Models of decision trees, regression and neural networks are used to predict various risk categories. This book shows you how to build decision tree models to predict a categorical target and how to build regression tree models and neural network models to predict a continuous target. Successive chapters present examples that clarify the application of the models in the field of risk. The examples are solved step by step with SAS Enterprise Miner in order to make easier the understanding of the methodologies used. The book begins by introducing the basics of creating a project, manipulating data sources, and navigating through different results windows. Data Mining tools are used to build the main risk models: Decision Tree, Neural Network, and Regression.

Predictive Models to Risk Analysis With Neural Networks, Regression and Decision Trees
Predictive Models to Risk Analysis With Neural Networks, Regression and Decision Trees

Author: Scientific Books

Publisher: Createspace Independent Publishing Platform

Published: 2016-01-01

Total Pages: 222

ISBN-13: 9781523211685

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The essential aim of this book is to use predictive models to analyze risk. Models of decision trees, regression and neural networks are used to predict various risk categories. This book shows you how to build decision tree models to predict a categorical target and how to build regression tree models and neural network models to predict a continuous target. Successive chapters present examples that clarify the application of the models in the field of risk. The examples are solved step by step with SAS Enterprise Miner in order to make easier the understanding of the methodologies used. The book begins by introducing the basics of creating a project, manipulating data sources, and navigating through different results windows. Data Miming tools are used to build the main risk models: Decision Tree, Neural Network, and Regression. These are addressed in considerable detail, with numerous examples of practical business applications that are illustrated with tables, charts, displays, equations, and even manual calculations that let you see the essence of what Enterprise Miner is doing when it estimates or optimizes a given model.

AI Based Techniques for Predictive Modeling
AI Based Techniques for Predictive Modeling

Author: Kharade S K

Publisher:

Published: 2023-05-16

Total Pages: 0

ISBN-13:

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AI-based techniques for predictive modeling involve using algorithms and machine learning techniques to analyze large amounts of data and identify patterns that can be used to predict future outcomes or trends. These techniques can be applied in a wide range of industries and applications, from finance and marketing to healthcare and manufacturing. One of the key advantages of AI-based predictive modeling is that it can identify patterns and trends that might not be immediately apparent to humans. These algorithms can analyze vast amounts of data from multiple sources, including historical data, real-time data, and external factors, to identify patterns and predict future outcomes with a high degree of accuracy. Some common techniques used in AI-based predictive modeling include decision trees, neural networks, and regression analysis. Decision trees are a type of algorithm that uses a hierarchical tree structure to identify patterns and relationships between variables. Neural networks, on the other hand, are modeled after the structure of the human brain and can identify complex patterns and relationships between variables. Regression analysis is another common technique used in predictive modeling that involves analyzing the relationship between two or more variables to predict future outcomes. This technique is often used in financial forecasting and risk analysis. Overall, AI-based techniques for predictive modeling offer significant benefits for businesses and organizations looking to make data-driven decisions. By analyzing large amounts of data and identifying patterns and trends, these techniques can help organizations predict future outcomes and make informed decisions that can improve efficiency, productivity, and profitability.

Data Mining and Predictive Analytics
Data Mining and Predictive Analytics

Author: Daniel T. Larose

Publisher: John Wiley & Sons

Published: 2015-03-16

Total Pages: 826

ISBN-13: 1118116194

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Learn methods of data analysis and their application to real-world data sets This updated second edition serves as an introduction to data mining methods and models, including association rules, clustering, neural networks, logistic regression, and multivariate analysis. The authors apply a unified “white box” approach to data mining methods and models. This approach is designed to walk readers through the operations and nuances of the various methods, using small data sets, so readers can gain an insight into the inner workings of the method under review. Chapters provide readers with hands-on analysis problems, representing an opportunity for readers to apply their newly-acquired data mining expertise to solving real problems using large, real-world data sets. Data Mining and Predictive Analytics: Offers comprehensive coverage of association rules, clustering, neural networks, logistic regression, multivariate analysis, and R statistical programming language Features over 750 chapter exercises, allowing readers to assess their understanding of the new material Provides a detailed case study that brings together the lessons learned in the book Includes access to the companion website, www.dataminingconsultant, with exclusive password-protected instructor content Data Mining and Predictive Analytics will appeal to computer science and statistic students, as well as students in MBA programs, and chief executives.

Predictive Modeling with SAS Enterprise Miner
Predictive Modeling with SAS Enterprise Miner

Author: Kattamuri S. Sarma

Publisher: SAS Institute

Published: 2017-07-20

Total Pages: 574

ISBN-13: 163526040X

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« Written for business analysts, data scientists, statisticians, students, predictive modelers, and data miners, this comprehensive text provides examples that will strengthen your understanding of the essential concepts and methods of predictive modeling. »--

Artificial Intelligence for Business Analytics
Artificial Intelligence for Business Analytics

Author: Felix Weber

Publisher: Springer Nature

Published: 2023-03-01

Total Pages: 146

ISBN-13: 365837599X

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While methods of artificial intelligence (AI) were until a few years ago exclusively a topic of scientific discussions, today they are increasingly finding their way into products of everyday life. At the same time, the amount of data produced and available is growing due to increasing digitalization, the integration of digital measurement and control systems, and automatic exchange between devices (Internet of Things). In the future, the use of business intelligence (BI) and a look into the past will no longer be sufficient for most companies.Instead, business analytics, i.e., predictive and predictive analyses and automated decisions, will be needed to stay competitive in the future. The use of growing amounts of data is a significant challenge and one of the most important areas of data analysis is represented by artificial intelligence methods.This book provides a concise introduction to the essential aspects of using artificial intelligence methods for business analytics, presents machine learning and the most important algorithms in a comprehensible form using the business analytics technology framework, and shows application scenarios from various industries. In addition, it provides the Business Analytics Model for Artificial Intelligence, a reference procedure model for structuring BA and AI projects in the company. This book is a translation of the original German 1st edition Künstliche Intelligenz für Business Analytics by Felix Weber, published by Springer Fachmedien Wiesbaden GmbH, part of Springer Nature in 2020. The translation was done with the help of artificial intelligence (machine translation by the service DeepL.com). A subsequent human revision was done primarily in terms of content, so that the book will read stylistically differently from a conventional translation. Springer Nature works continuously to further the development of tools for the production of books and on the related technologies to support the authors.

Optimized Predictive Models in Health Care Using Machine Learning
Optimized Predictive Models in Health Care Using Machine Learning

Author: Sandeep Kumar

Publisher: John Wiley & Sons

Published: 2024-02-08

Total Pages: 388

ISBN-13: 1394175353

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OPTIMIZED PREDICTIVE MODELS IN HEALTH CARE USING MACHINE LEARNING This book is a comprehensive guide to developing and implementing optimized predictive models in healthcare using machine learning and is a required resource for researchers, healthcare professionals, and students who wish to know more about real-time applications. The book focuses on how humans and computers interact to ever-increasing levels of complexity and simplicity and provides content on the theory of optimized predictive model design, evaluation, and user diversity. Predictive modeling, a field of machine learning, has emerged as a powerful tool in healthcare for identifying high-risk patients, predicting disease progression, and optimizing treatment plans. By leveraging data from various sources, predictive models can help healthcare providers make informed decisions, resulting in better patient outcomes and reduced costs. Other essential features of the book include: provides detailed guidance on data collection and preprocessing, emphasizing the importance of collecting accurate and reliable data; explains how to transform raw data into meaningful features that can be used to improve the accuracy of predictive models; gives a detailed overview of machine learning algorithms for predictive modeling in healthcare, discussing the pros and cons of different algorithms and how to choose the best one for a specific application; emphasizes validating and evaluating predictive models; provides a comprehensive overview of validation and evaluation techniques and how to evaluate the performance of predictive models using a range of metrics; discusses the challenges and limitations of predictive modeling in healthcare; highlights the ethical and legal considerations that must be considered when developing predictive models and the potential biases that can arise in those models. Audience The book will be read by a wide range of professionals who are involved in healthcare, data science, and machine learning.

Intelligent Systems: Concepts, Methodologies, Tools, and Applications
Intelligent Systems: Concepts, Methodologies, Tools, and Applications

Author: Management Association, Information Resources

Publisher: IGI Global

Published: 2018-06-04

Total Pages: 2390

ISBN-13: 1522556443

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Ongoing advancements in modern technology have led to significant developments in intelligent systems. With the numerous applications available, it becomes imperative to conduct research and make further progress in this field. Intelligent Systems: Concepts, Methodologies, Tools, and Applications contains a compendium of the latest academic material on the latest breakthroughs and recent progress in intelligent systems. Including innovative studies on information retrieval, artificial intelligence, and software engineering, this multi-volume book is an ideal source for researchers, professionals, academics, upper-level students, and practitioners interested in emerging perspectives in the field of intelligent systems.