Prediction on Impact of Electronic Gadgets in Students Life using Machine Learning

Abstract

Nowadays electronic gadgets play an important role in students' life as a source of learning. The Dependency of services provided by electronic gadgets has reached a large scale. Electronic gadgets like smart phones have a major impact on people in their day-to-day life. Among all, students are the important one, as they rely on electronic gadgets for their academic activities. The major impact is that it can affect the students mental and physical health. Students are getting addicted to these electronic gadgets as it becomes inevitable. This study uses machine learning techniques to demonstrate how gadgets affect students' daily lives. To examine the addictiveness of gadgets among the students, a questionnaire has been circulated to get to know the student's necessity on electronics. The parameters include how many electronic devices they use and how long they use them for, whether the usage of electronic gadgets shows any improvement in their academic performance. Machine learning employs the pre-programmed algorithms, to predict output values for the given input data. It is considered to be an aspect of artificial intelligence. Machine learning algorithms are used in a variety of fields, such as computer vision, voice recognition, medicine etc. Where it is difficult or impractical to create conventional algorithms to perform the necessary tasks. The collected dataset is taken to analyse the performance of prediction for various Machine Learning algorithms like K-Nearest Neighbour, Random Forest, Decision Tree, Logistic regression, Support Vector Machine. As a result of this study, accuracy of several performance measures were evaluated. In the future the performance of an algorithm can be improved by using optimization techniques.