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International Journal of Applied Research
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ISSN Print: 2394-7500, ISSN Online: 2394-5869, CODEN: IJARPF

IMPACT FACTOR (RJIF): 8.4

Vol. 7, Issue 8, Part D (2021)

Linear with polynomial regression: Overview

Linear with polynomial regression: Overview

Author(s)
Siddhant Patil and Shruti Patil
Abstract
In the current world, there is a need to analyze and define the relations from the data and predict outcomes for profits. Regression is a Machine Learning technique that involves finding correlations between variables and predicts a continuous output. It helps us to understand how the value of the dependent variable (target) is changing corresponding to an independent variable (predictor). The aim of this paper is to discuss linear regression with its types, polynomial regression, and the relationship between Linear Regression and Polynomial Regression and how they are interrelated. These techniques are widely used to find the trends in data and forecast some outcomes. The aforementioned techniques are explained and analyzed based on the factors like the size of the dataset, type of the data set, quality, efficiency, consistency, accuracy, variables, and performance. These methods create a visual graph that can be used for predicting various past and future outcomes. The intent of discussing the relationship between the techniques is to assist new researchers and beginners to understand how they function, so they can come up with new approaches and innovations for improvement.
Pages: 273-275  |  1582 Views  238 Downloads


International Journal of Applied Research
How to cite this article:
Siddhant Patil, Shruti Patil. Linear with polynomial regression: Overview. Int J Appl Res 2021;7(8):273-275. DOI: 10.22271/allresearch.2021.v7.i8d.8876
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