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@article{168518,
        author = {Prathamesh Ram Jamdae and Saloni Vitthal Jenekar and Omkar Anil Jadhav and Rushikesh Wamanrao Dalvi},
        title = {Performance and Mentorship of Engineering Students},
        journal = {International Journal of Innovative Research in Technology},
        year = {2024},
        volume = {11},
        number = {5},
        pages = {1197-1201},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=168518},
        abstract = {In order to improve the quality of education and give at-risk kids appropriate interventions, it is essential to forecast their academic success. In order to forecast student success based on a variety of characteristics, such as academic, social, and demographic ones, this study investigates the use of different machine learning models. The goal of the study is to identify the model that provides the highest prediction accuracy by evaluating models like Random Forest, Decision Trees, Support Vector Machines (SVM), and Neural Networks. The main elements that have the biggest effects on student outcomes are also found via feature importance analysis. The findings show that machine learning is capable of accurately predicting academic performance, which enables teachers to put focused support plans into place and raise overall student achievement. This study has consequences for the creation of flexible learning settings.},
        keywords = {Academic Performance Prediction, Machine Learning, Student Success, Random Forest, Support Vector Machines, Educational Data Mining.},
        month = {October},
        }