There are different sorts of prediction challenges in Machine Learning that are based on supervised and unsupervised learning. Classification, regression, grouping, and association are the four methods. We’ll talk about classification and regression in this section.
Classification: In classification, we aim to build a Machine Learning model that helps us categorize data into distinct groups. Based on the input parameters, the data is labeled and classified.
Consider the following scenario: we want to make predictions about the churning out of consumers for a specific product based on some data. Customers will either churn out or they will not. So ‘Yes’ and ‘No’ would be the labels for this.
Regression: Regression is the process of converting data into continuous real values rather than utilizing classes or discrete values to create a model. Based on past data, it may also determine dispersion movement. It’s used to forecast the occurrence of an event based on the degree of correlation between variables.
Temperature, air pressure, solar radiation, the height of the location, and distance from the sea, for example, all influence weather forecasting. The relationship between these variables aids us in forecasting the weather.