Implementation and Evaluation of Perceptron Model on Dataset

Abstract

This paper includes training the perceptron model utilizing stochastic gradient descent on the Specific Absorption Rate (SAR) Dataset. The perceptron model is broadly utilized in Artificial neural networks, artificial nets, deep learning, and classification problems. The perceptron calculation is utilized for the supervised learning of binary classifiers. A binary classifier is a function that tells whether info, introduced by a vector of numbers, has a place with a specific class. The rate at which electromagnetic radiations created from mobiles and pinnacles are consumed by the body is called as Specific Absorption Rate (SAR). The value is a proportion of the most extreme transmitted energy consumed by a unit of uncovered body tissue/cell of a human utilizing a versatile, throughout a period or more. SAR esteems are typically estimated in units of watts/kilogram (W/kg) in 1g-10g of body mass. The dataset depicts the SAR esteem consumed by the human body. There are three principal steps associated with this task viz. Making Predictions, Training network loads, and demonstrating the Specific Absorption Rate (SAR) Dataset.