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@article{194281,
        author = {Umamaheswararao Mogili},
        title = {An Intelligent Data-Driven Framework for Student Placement Prediction Using Machine Learning},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {10},
        pages = {4070-4078},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=194281},
        abstract = {Student placement is one of the most important factors that determine the future career opportunities of students after completing their academic programs. Educational institutions aim to ensure that a maximum number of students secure employment through campus recruitment drives conducted by various organizations. However, predicting which students are likely to be placed is a challenging task because it depends on several factors such as academic performance, technical knowledge, communication skills, problem-solving ability, and overall confidence. When the number of students and data records increases, analyzing these factors manually becomes difficult and time-consuming. In order to overcome these limitations, the Student Placement Prediction System is developed by utilizing machine learning techniques to analyze student data and predict placement outcomes. The proposed system collects important information related to students, including academic records, technical skill levels, aptitude abilities, and interview responses. These attributes are processed using data pre-processing techniques to remove inconsistencies, handle missing values, and convert the data into a suitable format for machine learning analysis. After pre-processing the dataset, a machine learning algorithm is applied to train the prediction model using historical placement data. The trained model learns patterns and relationships between various student attributes and their placement status. Based on this learned information, the system can predict whether a student has a higher probability of getting placed or not. The Student Placement Prediction System also provides valuable insights that help students understand their strengths and identify areas that need improvement. Students can analyze their performance in different areas such as communication, technical knowledge, and interview preparation. Placement coordinators and educational institutions can also use this system to identify skill gaps among students and design appropriate training programs to improve their employability. By applying machine learning techniques to educational data, the system provides a data-driven approach for predicting placement outcomes and improving the overall effectiveness of campus recruitment preparation.},
        keywords = {Machine Learning, Educational Data Mining, Logistic Regression, Random Forest, Predictive Analytics.},
        month = {March},
        }