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When we are a beginner in the Machine Learning field, we often get confused with classification and regression analysis. Regression is applied to the problem when a real or continuous value needs to be predicted, such as “salary” or “prices of the houses”. In these problem statements the target value is continuous and can be classified into a “yes” or “no” category. In such cases, we need to apply regression techniques. In this blog, I will cover the basics of different regression techniques and it’s python implementation.

What is Regression?

Regression is a statistical approach that understands the possible relationship among variables. The study of regression clarifies the changes in parameters in relation to changes in the target predictors. To investigate or analyze the relationship between the dependent and independent set of variables, regression methods are applied. It covers the variety of data analysis techniques that are implemented in qualitative-exploratory research for analyzing infinite variables. The prime applications of regression analysis are for forecasting, time series analysis modeling, and defining cause-effect relationships. …


Dr. Monica

Research aspirant in Machine learning and Data Science

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