A Machine Learning Approach to Identify Students Affected in Bangladesh Using Mobile Phone

Abstract

The titled “A Machine Learning Approach to Identify Students Affected in Bangladesh Using Mobile Phone” the study focuses on a mobile device with powerful hardware a capability that enables a wide range of software, internet and multimedia functionality such as music, video, camera and gaming in one unit. It is certainly a very useful device for us. Today, most of the students are using smart phones for a large part of the day to fulfill their goals like education, gaming, watching videos, accessing social media, listening to music etc. Of these, the use of mobile phones is considered to be extremely helpful for learning and education. But when they spend hours on it, including other factors, it can prove to be very detrimental to them. They are exposed to some health risks. In addition, many of them are dependent on technology and are addicted to smart phones. Now, an effective way to identify these effects is to ask a burning question over time. I use the XGBoost classification here to categorize the effects on academic outcomes, health risks, family relationships and gender issues. This system can be used by mobile phones to identify the effects that students are experiencing.