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.