The simplest kind of a neural network is a perceptron, which is the computational prototype of a neuron. In machine learning, the concept of a perceptron is crucial. It's used as a method or a linear classifier to help binary classifiers learn supervised. One of the most explored learning issues is supervised learning. An input and an appropriate output are always present in a supervised learning sample. The goal of this learning challenge is to use information with correct labels to train a model and make predictions on future data. Classification to estimate class labels is one of the most common supervised learning issues.