Supervised learning is a popular method of machine learning that involves training an algorithm on a labeled dataset. The dataset contains input data that is associated with known output values. Using this labeled dataset, the algorithm learns to map the input data to the output values, which enables it to predict the output values for new input data. This ML model is usually based on the Train-Test procedure. The supervised learning tasks are divided into two categories: Regression and classification. The classification model only classifies the statement either as true or false. On the other hand, the Regression model gives the numerical value as the result. The most popular examples of supervised learning methods include regression analysis, decision trees, and support vector machines.