Glossary of terms
Prescriptive analytics is a natural successor to predictive analytics. When we perform predictive analytics, it is (not surprisingly) to tell us something about the future. At its core, it is a branch of analytics that uses data, algorithms, and machine learning to make recommendations and optimal decisions. It examines past and current data to predict future events and develop strategies for action. Prescriptive analytics helps to solve complex problems by taking into account constraints and conditions, which allows for informed decision-making. It is widely used in various industries, including business, medicine, finance, and others, to optimize processes and achieve better results.
The history of prescriptive analytics goes back more than half a century when scientists began using mathematical models to make decisions in management and economics. Since then, the field has been growing and evolving, providing companies with the ability to use data to optimize processes and achieve better results.
Prescriptive analytics is a key branch of analytical science that aims to predict optimal decisions and recommendations based on data analysis.
It uses algorithms and models to predict possible scenarios and outcomes.
Provides recommendations for optimal actions based on predictions and inputs.
Helps solve problems and make decisions by finding the best options
Takes into account constraints such as budget, resources, and time when making recommendations.
Prescriptive analytics can be divided into several types, depending on the methods and techniques used to utilize the data.
The use of mathematical models to find optimal solutions.
Using machine learning algorithms to make predictions and recommendations.
Using computer models to simulate scenarios and evaluate the impact of different decisions.
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