An Analysis upon Polynomial Regression Modelling using Maching Learning Approach: A Review: Advancements in Polynomial Regression Modeling and Learning Approaches

Abstract

In the thesis, we address the errand of polynomial regression, i.e., prompting regression models dependent on polynomial equations, from information. We go for enhancing and stretching out the current approaches to learning polynomial regression models in a few headings. First, we enhance the current methods for tending to the issue of over-fitting and enhance the current methods for requesting the hunt space of competitor polynomial equations. Second, we expand the extension of existing methods towards learning piecewise, multi-target, and classification through regression polynomial models. We likewise guess that their execution will be equivalent to the execution of models got with other best in class regression and classification approaches.