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Regression models predict a continuous output variable from "summary" of Data Science for Business by Foster Provost,Tom Fawcett
Regression models are a fundamental tool in data science for predicting continuous output variables. In simple terms, this means that regression models are used when the target variable we want to predict is a number, rather than a category or class. For example, if we want to predict the price of a house based on its features, such as size, location, and number of bedrooms, we would use a regression model. The goal of a regression model is to find the relationship between the input variables (also known as features) and the output variable. This relationship is typically represented by a mathematical equation that describes how the input variables influence the output variable. The process of building a regression model involves training the model on a dataset where both the input variables and the output variable are known. The model then learns from this data and us...
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