When it comes to data science, linear and logistic regression are two topics that every data scientist learns at a very early stage of their data science career. They are crucial and have a wide range of applications. Due to their popularity, a lot of data scientists end up doing regression analysis for better data analysis. There are about 7 techniques of regression analysis. Each of them has its importance and cases to produce high-quality analysis.

In this article, I will explain to you about 7 different regression techniques and their importance in data-driven technologies. Let us dive in and explore more in detail.

What is Regression Analysis?

Regression analysis is a form of predictive modeling technique that defines the relationship between a dependent variable and an independent variable. The prime purpose of using this technique is forecasting, time series analysis, and finding the relationship between variables. One of the best examples is the relationship between the number of people getting COVID positive and the number of people dying daily.

Regression Analysis plays a crucial role in modeling and analyzing data.

Types Of Regression Analysis

Linear Regression

Linear Regression is one of the most popular modeling techniques. A lot of data scientists choose to learn linear regression techniques while learning predictive modeling. In this modeling, the dependent variable is continuous, and the independent variable may be continuous, or discrete while the nature of the regression line is linear.

Logistic Regression

Logistic regression uses for finding the probability of event = success and event = failure. When the dependent variable is binary, that means it has two outcomes (0/1, True/False, Yes/No). Logistic regression uses for the classification problem. Logistic regression doesn’t depend upon the linear relationship between dependent and independent variables.

Polynomial Regression

An equation is said as a polynomial regression equation when the power of the independent variable is more than one. The main reason for using polynomial regression is to reduce the error.

Stepwise Regression

In the stepwise regression method, we deal with multiple independent variables. In this technique, an automatic process uses for finding dependant variables. Here it adds or removes predictors as needed for each step. Whereas in forward, it adds another variable, and in backward, it omits another variable.

Ridge Regression

In this method, the prediction errors decomposed into two sub-components. First, due to the biased and secondly due to the variance. Ridge regression solves the multicollinearity problem through shrinkage parameters known as lambda.

Lasso Regression

In lasso regression, lasso stands for Least Absolute Shrinkage and Selection Operator. The prime cause of using this regression technique is to reduce the variability and increase the efficiency of the linear regression model.

ElasticNet Regression

ElasticNet regression model is the hybrid model of the ridge model and the lasso model. There are no limitations to the number of selected variables. Whereas, it can suffer from double shrinkage.

These were about regression analytics techniques. if you want to learn more in details, reach out to ExcelR Solutions for the best data science training in Hyderabad. When you join ExcelR Solutions for the best data science course in Hyderabad, you get lifetime access to self-paced learning, 100% placement assistance, and many more facilities at zero-cost EMI. For more, visit our website at www.excelr.com.

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