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Polynomial Regression is a powerful machine learning technique used to model non-linear relationships between independent and dependent variables. It is a type of regression analysis that can be used to fit a polynomial equation to a set of data points. This guide provides an overview of the fundamentals of polynomial regression, including the types of problems it can be used to solve, the assumptions it makes, and the techniques used to fit a polynomial equation to a set of data points. It also provides an overview of the different types of polynomial regression models, as well as tips and tricks for improving the accuracy of your model. Finally, it provides a comprehensive guide to the implementation of polynomial regression in Python.
