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Predictive Modeling in R | Predictive Analytics | Data Analytics for Beginners | Great Learning

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Predictive modeling is the process of using known results to create, process, and validate a model that can be used to forecast future outcomes. Predictive analysis is heavily used today to gain insights on a level that are not possible to detect with human eyes. And R is an extremely powerful and easy tool to implement the same. Understanding all of this, we have come up with this tutorial on “Predictive Modeling In R”

In R programming, predictive models are extremely useful for forecasting future outcomes and estimating metrics that are impractical to measure. For example, data scientists could use predictive models to forecast crop yields based on rainfall and temperature or to determine whether patients with certain traits are more likely to react badly to a new medication.

In other words, Predictive modeling is the general concept of building a model that is capable of making predictions. Typically, such a model includes a machine learning algorithm that learns certain properties from a training dataset in order to make those predictions. In this tutorial, we will help you to understand everything related to Predictive Modeling In R, such as, What is Predictive Modeling, How Predictive Modeling In R can be used, What are its features, and much more.

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