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@article{209784,
        author = {Ms.Snehal Nandkishor Dhulgande and Ms.Ankita Tirupati Bansode and Ms.Sandhyarani Navnath Bansude and Ms.Jyoti Dnyaneshwar Gadadare and Mrs.Tanuja S Dhage},
        title = {AI-Based Student Campus Placement Prediction System A Web Application for Predicting Student Placements Using Machine Learning Algorithms},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {13},
        number = {5},
        pages = {3199-3207},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=209784},
        abstract = {Campus placement is an important stage in the transition from higher education to employment. Students often want to understand how their academic and experience-related profiles may relate to placement outcomes, while institutions may seek ways to use historical records to support career guidance. This paper presents a Campus Placement Predictor web application that uses machine-learning classification to estimate a placement outcome from selected student attributes. The project presentation identifies age, gender, academic stream, number of internships, CGPA, hostel status, and history of backlogs as input features, with “placed or not” as the target label. The proposed implementation uses Python and machine-learning libraries such as NumPy, Pandas, Matplotlib, and Scikit-learn, with Flask connecting the prediction model to a frontend developed using HTML, CSS, and JavaScript. The candidate classifiers are Logistic Regression, Support Vector Classifier, K-Nearest Neighbours, Decision Tree, Random Forest, and Gradient Boosting. The intended process is to inspect and prepare the data, train candidate models, compare their performance, select an appropriate model, and integrate it into the web application. The supplied presentation does not provide the dataset size, data source, train/test split, measured metrics, or final best-performing classifier. Therefore, this paper explains the system and evaluation plan without inventing numerical results. The application should be understood as an advisory tool for exploring patterns in data, not as a guarantee of an individual student’s employment outcome.},
        keywords = {},
        month = {October},
        }