Classifying Gender Based on Life Partner Choosing Factor using Supervised Machine Learning

Abstract

Nowadays, gender Prediction has become a popular subject in machine learning and predicting gender by analyzing some text or names is very common while predicting gender by their taste or favourites is not so popular. As a result, for this paper, we established an aim of predicting people's gender based on their preferences or requirements. There are a lot of choices and desires we want in our life partner, that’s why it was easy to detect gender on the basis of our choices. From ‘data storing’ to ‘selecting a model’ we have followed a modern workflow. We have made a public survey with proper questionnaires and encompass 758 data from different persons and tried to know their choices of choosing a life partner. We have tested our datum with 8 different Machine Learning Algorithms and from these algorithms, five algorithms - Gradient Boosting Classifier (GBC), Stochastic Gradient Classifier, eXtreme Gradient Boost (XGB), Decision Tree Classifier (DT), Random Forest (RF) comprises favorable accuracy from 90%-95.39%. The best correctness we have found is from the RF machine learning algorithm with 95.39% accuracy. The model can be a useful notion to apply in any Life Partner Chosen type applications (e.g., Wedding Service-Shaadi.com), according to the approach that arises from this study.