Response:  Classification and Regression are both used for Supervised Learning cases.  Classification produces discrete values to classify or categorize the target (e.g. fail/No-fail etc.) whereas regression provides a continuous result that allows us to distinguish between various point values effectively.  Hence, in a dataset, if the target variable is continuous, then “regression” will be used. If the target variable is categorical, then “classification” will be used.  If we wanted to predict whether a machine will fail or not in future, we will use classification. If we want to predict the height of a person based on other relative attributes where target is a number and continuous of nature, then we will use regression.  Of course, there are different types of regression and they are not same and have different techniques to solve different type of business problems.