Performance Measurement of Multiple Supervised Learning Algorithms for Gender Identification from Bengali Names

Gender recognition of names is today regarded as one of the most significant problems in data extraction. Machine learning and NLP are used to solve this. Recognition of the gender of a name relies heavily on various kinds of NLP applications such as Question addressing systems, Text summarization etc. Throughout this article, we will investigate the Bengali name entity and attempt to extract and recognize the Person's name entity to determine if the name is male or female. Most people's names have a sexual orientation refinement. However, determining genders from Bengali names with greater accuracy might be difficult. We will demonstrate an artificial intelligence-based characterization technique that can accurately determine sexual orientations from Bengali names. We'll use a variety of algorithms to figure out which computations get the best results. We attained an accuracy of 84 percent by using Naive Bayes.