Browsing by Author "Mahbuba, Ashrarfi"

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    A Machine Learning Approach to Find Students’ Satisfaction From Home Vs Hostel in Bangladesh
    (Daffodil International University, 2021-01-31) Nooder, Jarin; Mahbuba, Ashrarfi
    Students are the backbone of a nation. We have to offer them the best requirements they need for their study. Among all these needs, the most important one is where they can get the perfect atmosphere as per their requirements. Our research title “MACHINE LEARNING APPROACH TO FIND STUDENTS’ SATISFACTION FROM HOME VS HOSTEL IN BANGLADESH” focuses on finding out the best place to study among the students living with their parents and those in hostels. This study also concentrates on factors like the living environment with learning resources and facilities of students. 400 students' responses were measured through adapted questionnaires from different schools, colleges, universities, and recently graduated students. The results of the analysis reveal that students prefer a home as it is safe and comfortable for living and study. We have used many algorithm techniques but we preferred the Logistics Regression Algorithm the most for this research as we have got the best accuracy rate in this. So we can say that students prefer home mostly.
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    Machine Learning Approach to Find Students' Best Place to Study
    (2021 2nd International Conference on Innovative and Creative Information Technology (ICITech), IEEE, 2021-11-15) Nooder, Jarin; Mahbuba, Ashrarfi; Sharmin, Shayla; Moon, Nazmun Nessa; Poushy, Lamisha Haque; Bhuiyan, Salauddin Ahmed; Nawshin, Samia
    Students are a country's backbone. The appropriate surroundings for studying must be provided for them. Of all these criteria, a place where you may locate the appropriate setting for your requirements is the most important. The purpose of the study is to identify the best environment to study among students living with parents and hostels. This research also explores issues such as the life and academic chances of students. Adapted questionnaires were utilized to evaluate the responses of 400 students from different colleges, institutes, and students freshly graduated. According to the findings of the survey, students choose to live and study at home because it is healthy and convenient. A variety of algorithm techniques are used, but the Logistics Regression algorithm was the key preference for this study because it had the highest accuracy score. This leads to the conclusion that students opt to stay at home