Abstract

Regression analysis is an important and simple technique in machine learning, which entails determining the optimal line that intersects the original data points to determine the strength of the relationship between one or more independent variables and the dependent variable. Multiple linear regression analysis can be used to describe this relationship between variables based on their scores. There are a variety of algorithms used in supervised learning methods. In recent years, a large number of supervised learning methods have been introduced into machine learning. Supervised learning techniques have become a field of scientific research activities and are applied in processing and analyzing various data sets, which is called the regression approach. It has become one of the most critical features of supervised machine learning, with the ability to analyze available data and future predictions. The supervised machine learning method and the regression method of all kinds are the two basic techniques in which we will cover the basic aspects of the topic. Whereas, machine learning is concerned with the field of predictive modeling, reducing model error, or making predictions more accurate than they can be at the expense of interpretability. In machine learning applications, we will use algorithms and replicate their use in various other fields, including statistical science, and how to investigate them. In this field, linear regression has been studied and developed as a model to understand the multiple relationships between variables of inputs and outputs. It is a machine learning algorithm and a statistical algorithm.