In Machine Learning, there are various types of prediction problems based on supervised and unsupervised learning. These are classification, regression, clustering, and association. Here, we will discuss classification and regression.
Classification: In classification, we try to create a Machine Learning model that assists us in differentiating data into separate categories. The data is labeled and categorized based on the input parameters.
For example, imagine that we want to make predictions on the churning out customers for a particular product based on some data recorded. Either the customers will churn out or they will not. So, the labels for this would be ‘Yes’ and ‘No.’
Regression: It is the process of creating a model for distinguishing data into continuous real values, instead of using classes or discrete values. It can also identify the distribution movement depending on the historical data. It is used for predicting the occurrence of an event depending on the degree of association of variables.
For example, the prediction of weather conditions depends on factors such as temperature, air pressure, solar radiation, the elevation of the area, and distance from the sea. The relation between these factors assists us in predicting the weather condition.