Campus Placement Eligibility & Recruitment Outcome Prediction Using Academic and Behavioural Attributes

Abstract: Campus placement outcomes influence both students career and also the academic situation of education institutions. In the recent years, employment market has become very competitive and difficult for training and placement departments to assess the students accurately for the placements using traditional evaluation methods. To address this challenge, this system is presenting a machine learning-driven framework for predicting the campus placements and its eligibility by evaluating academic and behavioural attributes. This proposed approach uses features such as cumulative grade point average (CGPA), students technical skills, internship experience, and also communication capabilities to estimate placement probability. The system follows a structured pipeline that includes data collection, data preprocessing, feature selection, train test split, machine learning models, model evaluation and Placement predicted outcome. Data preprocessing techniques can be used to reduce this. Correct inconsistencies, encode categorical variables, and normalize numerical features, enabling the models to be built. Thus, a number of different classification algorithms are implemented and tested to see which works best Prediction. The framework is implemented in the python programming language and the pandas library is used. And analysis, and Scikit-learn can be used for modelling and evaluating the approach. Experimental Analysis shows that combining both academic and behavioural data can yield better results. Accuracy prediction as compared to models that only use the academic performance for prediction. The result experimental and communication-related features play an important role in improving employability and placements preparation. The proposed system provides actionable insights that can help training and placement departments to help identify students that need direct skill instruction and to target those needing specific skills programs or grouping like skilled students together for training. By leveraging machine learning and data- driven decision making, this research contributes to improving placement preparation strategies and optimizes institutional placement outcomes.

Index Terms: campus placement eligibility, python, pandas, Scikit-learn, classification algorithms.