Work place: Maharaja Agrasen University, Solan, Himachal Pradesh, India
Research Interests: Video Processing, Data Analysis, Machine Learning
Nidhi Walia is a PhD scholar in Maharaja Agrasen University, Solan, India. Before coming into PhD, she had worked as Senior Research fellow in CSIR-CSIO, India. She has total of 6 years of experience in teaching and Research. Her main interest includes Machine learning, data analysis and Image/Video Processing. She has 4 years of teaching experience and published 10 research papers in different international journals and conferences.
DOI: https://doi.org/10.5815/ijmecs.2023.06.01, Pub. Date: 8 Dec. 2023
Predicting College placements based on academic performance is critical to supporting educational institutions and students in making informed decisions about future career paths. The present research investigates the use of Machine Learning (ML) algorithms to predict college students' placements using academic performance data. The study makes use of a dataset that includes a variety of academic markers, such as grades, test scores, and extracurricular activities, obtained from a varied sample of college students. To create predictive models, the study analyses numerous ML algorithms, including Logistic Regression, Gaussian Naive Bayes, Random Forest, Support Vector Machine, and K-Nearest Neighbour. The predictive models are evaluated using performance criteria such as accuracy, precision, recall, and F1-score. The most effective machine learning method for forecasting students' placements based on academic achievement is identified through a comparative study. The findings show that Random Forest approaches have the potential to effectively forecast college student placements. The findings show that academic factors such as grades and test scores have a considerable impact on prediction accuracy. The findings of this study could be beneficial to educational institutions, students, and career counsellors.[...] Read more.
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