Empirical Investigation into Social Media's Effects on Academic Performance: A Technical Perspective

Providing students with a top-notch education is the main goal of educational establishments. This study intends to use a variety of data mining tools in order to evaluate their effectiveness and determine the impact of different factors on students' academic performance. The res ults of this study establish the groundwork for well-informed decision-making for educators, parents, and legislators by advancing our understanding of the complex interactions between social media and academia. Through identifying plausible positive or negative correlations between social media engagement and academic performance, this research contributes to the development of tactics to optimize the advantages of digital platforms while reducing inherent risks. To examine the dataset, a variety of classification algorithms were used, such as decision trees, random forests, SVM classifiers, and LR classifiers.