Regression is a handy tool to find relationships between dependent variables and independent variables. However, we cannot always fit a regression line to any given dataset.
This type of model assumes that there exists a linear relationship between features and targets. This relationship is defined by a set of coefficients.
The inputs are the Data features that we’ve defined and the output is the target we’re trying to predict. This relationship is defined by a set of coefficients which are multipliers of each of the input features.
Linear regression in itself is very powerful if used properly.
It is a great tool to apply as the first model if applicable and can serve as benchmark to most the projects.
Linear regression algorithms are highly interpretable as they provide the relationship between input and output
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