Impact of the Use of Social Media Among University Students Using Machine Learning

Abstract

This study explores innovative machine learning approaches to investigate how social media affects student behavior. In this study, we have collected a good number of dataset from different students of our university and cleaned, encoded as well as used feature engineering on our raw dataset through different scikit-learn classes for better training outcomes. We have trained our dataset using different types of classifiers like Gradient Boosting, Random Forest, Multi-Layer Perceptron, AdaBoost and Decision Trees Classifiers. We have used k-fold cross-validation for proper evaluation and obtained a high accuracy of 93% for the Gradient Boosting Classifier by analyzing the performance using confusion matrix, representing Area Under the ROC Curve (AUC) and Receiver Operating Characteristic curve (ROC). This study will play a vital role in controlling the upcoming youngster in using their social media.