• Yash Madke
• Yash Urkude
• Kshitij Humane
• Ishanku Ukey
• Sanskruti Kokde

This study develops a machine learning-based placement prediction model using logistic regression to forecast student employability based on academic, technical, and experiential factors. The model utilizes a diverse dataset comprising student academic performance (GPA, test scores), technical skills (certifications, programming knowledge), extracurricular activities, and work experience (internships, part-time jobs) to estimate the probability of securing employment or placement. Logistic regression was chosen for its balance between simplicity, interpretability, and strong performance in binary classification tasks. Comprehensive data preprocessing steps, including feature selection, normalization, and handling of missing values and class imbalance, were conducted to optimize the model. Model performance was rigorously evaluated using metrics such as accuracy, precision, recall, F1-score, and ROC-AUC, ensuring a robust assessment of its predictive capability. enhancements are proposed, including the integration of more advanced machine learning algorithms, such as ensemble methods or neural networks, real-time data updates, and personalized feedback mechanisms for students. These improvements could increase the accuracy, scalability, and practical utility of the placement prediction system, helping institutions make data-driven decisions to support student employability and career readiness. The study underscores the potential of predictive analytics to transform career services and student counselling by offering actionable insights into placement trends and influencing factors.

Machine Learning, Placement Prediction, HR Analytics, Workforce Readiness, Skill-Based Job Matching, Supervised Learning, Higher Education Analytics, Data-Driven Decision Making, Logistic Regression, Student Success, Career Outcomes

"Placement Prediction using Machine Learning", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.12, Issue 4, page no.n293-n297, April-2025, Available :http://www.jetir.org/papers/JETIR2504D37.pdf

"Placement Prediction using Machine Learning", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.12, Issue 4, page no. ppn293-n297, April-2025, Available at : http://www.jetir.org/papers/JETIR2504D37.pdf