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Paper Details

Classifying Gender Based on Life Partner Choosing Factor using Supervised Machine Learning
Md. Hasan Imam Bijoy, Abu Kowshir Bitto, Maksuda Akter Rubi, Masud Rabbani, Md. Mahbubur Rahman, Mehedi Hasan,

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, according to the approach that arises from this study.

Machine learning algorithms , Computational modeling , Buildings , Stochastic processes , Predictive models , Boosting , Data models
Journal or Conference Name
2022 13th International Conference on Computing Communication and Networking Technologies, ICCCNT 2022
Publication Year