Paper Title
An Empirical Comparisons of Different Regression Models with their Advantages and Disadvantages

Abstract
Machine learning is basically a part of artificial intelligence. it consists of different algorithms which helps computer to learn the different task and perform accordingly. Here we provide knowledge to the computers through data, observations and interacting with the world. Machine learning utilizes a used variety of algorithms and intelligently handle complex data sets to make good decisions for predictions.Once the machine learning model has been trained we can give different datasets to see if it is making the prediction right or wrong. It becomes essentially important to use machine learning in today's era because it can solve complicated real world problems in a scalable way. It can also give a prediction by processing its previous data sets and can predict the future as well.Machine learning its incredibly important nowadays.1.It can solve complicated real-world problems in a scalable way.2.It has disrupted a variety of industries within the past decade, and continues to do so in the future, as more and more industry leaders and researchers are specializing in machine learning.3. The rate in which machine learning is causing positive impact is already surprisingly impressive which have been successful thanks to the dramatic change on data storage and computing processing power as more people are increasingly becoming involved. Keywords - Machine Learning, Simple Linear Regression, Multiple Linear Regression, Polynomial Regression, Support Vector Regression, Decision Tree Regression, Random Forest Regression