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International Journal for Research Trends and Innovation
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ISSN Approved Journal No: 2456-3315 | Impact factor: 8.14 | ESTD Year: 2016
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Impact Factor : 8.14
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| Paper Title: | A Comprehensive Study on Predictive Modeling for Student Placement Using Machine Learning Algorithms |
| Authors Name: | Lakshay Khandelwal , Saransh Jaiswal , Mohit Punyani , Dr. Devesh Kumar Bandil , Dr. Vishal Shrivastava |
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IJRTI_203657
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| Published Paper Id: | IJRTI2505113 |
| Published In: | Volume 10 Issue 5, May-2025 |
| DOI: | |
| Abstract: | Student placement success is a crucial indicator for assessing the efficacy and standing of educational institutions in the highly competitive academic and job environment of today. In addition to enabling schools to offer focused interventions, predicting a student's chance of placement based on academic records, skill sets, demographic characteristics, and behavioral indications also helps students become more prepared for the workforce. This study explores how different supervised machine learning algorithms may be used to predict student placement results.The study is based on a solid dataset of past student profiles that includes technical and soft skills, employment experiences, certifications, extracurricular activities, academic achievement (grades, test scores), and aptitude test results. To guarantee high-quality input for model training, extensive data pre-processing techniques are used, such as normalization, addressing missing values, and encoding categorical variables. To find the most significant placement predictors, feature selection techniques like Recursive Feature Elimination (RFE) and correlation analysis are used Decision Tree, Naive Bayes, Random Forest, Logistic Regression, K-Nearest Neighbors (KNN), and Support Vector Machine (SVM) are among the categorization methods that are compared. To guarantee generalizability, each model is tested using k-fold cross-validation. The effectiveness of each model is assessed using performance measures including accuracy, precision, recall, F1-score, and ROC-AUC. With placement prediction accuracies of over 85%, ensemble learning techniques—in particular, Random Forest—consistently show higher predictive capacity across the studied models. These results highlight how resilient ensemble methods are when working with intricate, high-dimensional educational data. The paper also presents a web-based recommendation system prototype that incorporates the predictive model. Placement officers can identify at-risk students who can benefit from individualized skill development programs and mentorship thanks to this system's real-time insights.This study intends to help academic institutions make data-driven, well-informed decisions that improve student employability and maximize funding for training and development programs by utilizing machine learning's predictive capabilities. The suggested approach advances the developing fields of institutional analytics and educational data mining while acting as a springboard for more intelligent educational systems. |
| Keywords: | Student Placement Prediction, Machine Learning, Educational Data Mining, Random Forest, Naive Bayes, SVM, K-Nearest Neighbors, Logistic Regression, Classification Models, Predictive Analytics, Recommendation System, Data Preprocessing, Feature Selection, Accuracy Metrics, Employability Enhancement. |
| Cite Article: | "A Comprehensive Study on Predictive Modeling for Student Placement Using Machine Learning Algorithms", International Journal for Research Trends and Innovation (www.ijrti.org), ISSN:2455-2631, Vol.10, Issue 5, page no.b96-b101, May-2025, Available :http://www.ijrti.org/papers/IJRTI2505113.pdf |
| Downloads: | 000487 |
| ISSN: |
2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016 An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator |
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Published Paper ID: IJRTI2505113
Registration ID:203657
Published In: Volume 10 Issue 5, May-2025
DOI (Digital Object Identifier):
Page No: b96-b101 Country: Jaipur, Rajasthan, India Research Area: Engineering Publisher : IJ Publication Published Paper URL : https://www.ijrti.org/viewpaperforall?paper=IJRTI2505113 Published Paper PDF: https://www.ijrti.org/papers/IJRTI2505113 |
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ISSN: 2456-3315
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