Published Paper Details:

Abstract: The rapid evolution of Artificial Intelligence (AI) and Machine Learning (ML) techniques has paved the way for innovative applications across various domains, including human resources and recruitment. This research focuses on the development of a predictive model for campus placement, leveraging AI and ML algorithms to enhance the efficiency of the placement process. The proposed system aims to assist educational institutions and employers in making informed decisions by forecasting the likelihood of a student's success in securing a job during campus placements. The methodology involves the collection of comprehensive data sets, including academic performance, technical skills, extracurricular activities, and other relevant attributes, from previous placement cycles. Feature engineering is employed to extract meaningful patterns and relationships within the data, and a variety of supervised learning algorithms, such as Decision Trees, Support Vector Machines, and Neural Networks, are explored to build the prediction model. The system is designed to offer personalized insights for both students and recruiters. Students can receive feedback on areas of improvement, helping them enhance their employability skills. Recruiters, on the other hand, benefit from a streamlined and data-driven approach to shortlisting candidates, ultimately leading to more efficient and successful placement processes. The research also delves into the ethical considerations surrounding the use of AI in the hiring process, addressing potential biases and ensuring fairness in decision-making. Robust evaluation metrics, including accuracy, precision, recall, and F1 score, are employed to assess the model's performance and reliability. The proposed AI-ML-based campus placement prediction system has the potential to revolutionize the traditional placement process, fostering a more data-driven, transparent, and equitable approach for both educational institutions and employers. The study contributes to the ongoing discourse on leveraging advanced technologies to optimize various aspects of the recruitment ecosystem.