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What is Predictive Modeling

Handbook of Research on Policies, Protocols, and Practices for Social Work in the Digital World
It uses mathematical and computational methods to predict an outcome or event.
Published in Chapter:
Potential Threats of Social Engineering Practices to Social Work: Concepts, Challenges, and Solutions
Gurkan Tuna (Trakya University, Turkey) and Ebru Efeoglu (İstanbul Gedik University, Turkey)
DOI: 10.4018/978-1-7998-7772-1.ch016
Abstract
Social work aims to promote social change, development, cohesion, and the empowerment of people and communities. Its professionals work with institutions and families and help to provide and advance various aspects of human equity such as civil rights, unemployment insurance, worker's compensation, disability pay, medicaid and medicare, and child abuse and neglect prevention. In recent years, social engineering practices started to threaten not only just a single or group of individuals but also the entire society. Considering the potential threats of social engineering practices on the practices of social work, strategies and good practices should be developed to achieve the sustainable goals of social work. Since the social engineering practices target our fundamental biases, our ignorance, and our lack of cognition, the key to counteracting them is awareness. This chapter covers the potential threats of social engineering practices, challenges related to counteracting them, methodologies used to uncover them, and future research directions in this domain.
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Intelligent Engineering and Applied Sciences for Comprehensive Environmental Sustainability: An Interdisciplinary and Cross-Sectoral Approach
The use of statistical techniques and AI algorithms to forecast future outcomes based on historical data, such as predicting environmental changes.
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Automobile Fatal Accident and Insurance Claim Analysis Through Artificial Neural Network
A process that utilize historical and current data to capture patterns, then create models to make future predictions.
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Introduction to Data Science
Predictive modeling is a commonly used statistical technique to predict future behavior.
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Forecasting Energy Impact in Multifamily Buildings Through Airtightness Models
The process of using known results to create, process, and validate a model that can be used to forecast future outcomes.
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Value Analysis and Prediction Through Machine Learning Techniques for Popular Basketball Brands
A mathematical process that seeks to predict future events or outcomes by analyzing patterns that are likely to forecast future results (Carew, n.d.).
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Academic Analytics
The process of creating models that can be used to predict a decision, a ranking or an estimate.
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Variable Selection by Domain Experts vs. Filter Algorithms for Clinical Predictive Modeling
The process of developing and implementing models that can accurately predict the class of an observation whose class is unknown.
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US Medical Expense Analysis Through Frequency and Severity Bootstrapping and Regression Model
A commonly used statistical technique to predict future behavior. Predictive modeling solutions are a form of data-mining technology that works by analyzing historical and current data and generating a model to help predict future outcomes.
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CSE as Epistemic Technologies: Computer Modeling and Disciplinary Difference in the Humanities
Statistical method for determining the likelyhood that archaeological evidence will be found in a particul locale.
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Understanding the Human-Machine Interface in a Time of Change
The process of using patterns found in past data to identify potential risks and possibilities.
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Education in the Era of Industry 4.0: Qualifications, Challenges, and Opportunities
It is a process that forecasts certain events in manufacturing using data mining and probability.
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Interdisciplinary Application of Machine Learning, Data Science, and Python for Cricket Analytics
Using statistical algorithms and machine learning techniques to build models that can predict future outcomes.
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