Regression Analysis is a machine learning algorithm that we use to find out how closely independent variable(s) relates to the dependent variables. We use regression analysis to build models on datasets that have the potential to predict the accurate value of dependent variables.

In regression analysis, we use two groups of the database: a training dataset and a testing dataset. We use training datasets to create different models to find out the best approach to apply on the line that best fits into the graph. The line could be anything like a straight line, curve line but, that should easily fit the gap of the independent variable(s) VS dependent variables.

We can use the newly created model to predict the dependent variables of the testing datasets. Then the next step is the comparison. The comparison between the predicted dependent value and the original dependent variables using different accuracy measurement techniques like R-square methods, root mean square method, root mean average error and others. If the accuracy of the score is not up to the mark, and you want to build a stronger model. You can change the data variables (dependent and independent).

There is always an alternate way of building a stronger model if you feel unsatisfied with the previous model. Here all you have to do is change from linear regression analysis to other regression models like polynomial regression analysis or multiple linear regression analysis to multiple polynomial regression analysis.

Different regression analysis models for continuous variables such as simple linear regression, multiple linear regression, polynomial regression, multiple polynomial regression.

Types of Regression Analysis

Linear Regression

Linear regression is a simple approach to find the value of dependent variables using independent variables. Here the line that forms on the graph taking dependent and independent variables. You can test the value of datasets how closely they are to the actual data of the database.

Polynomial Regression

We use polynomial regression to find out the value of dependent variables to predict the value of the dependent variables. Here the figure obtained is mostly the curve that best fits the value of dependent and independent variables. Here the equation you may find falls on three categories: second degree, third degree, and fourth degree.

Multiple Linear Regression

Multiple linear regression is the advanced level method for using two or more independent variables to predict the value of the dependent variable. You can get multiple lines here that obtain a general equation from the training datasets that data scientists can use to predict the value of the testing dataset.

Multiple Polynomial Regression

Data Scientists use multiple polynomial regression models to find out the value of dependent variables using two or more independent variables. This method is quite the same as the multiple linear regression but with some advanced techniques and procedures.

It is all about what is regression and the different types of regression we use in data science. Want to learn more about regression analysis? Join ExcelR Solutions, for best data science training in Hyderabad and get lifetime access to the learning management system with all the updates in the future.

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