Name Gender Recognition System

Abstract

Person names are extremely important in various types of computer applications. The majority of people's names have a possible refinement between sexual orientations. Recognizing sexual orientations from English character-based Bangladeshi names with greater accuracy can be particularly difficult. In this paper, we present a characterization system focused on machine learning and deep learning that is capable of recognizing sexual introductions from Bangladeshi people's names. With an accuracy of 88 percent, the English character-based name was developed. We have compared various machine learning and deep learning classifiers, such as Logistic Regression, Random Forest, SVM, and others, to see which calculations provide the best results. Aside from that, a pre-trained python demonstrate on sexual orientation identifiable proof by Bangla's title was discovered.