Campus Placement Prediction And Analysis Using Machine Learning

In the highly competitive job market, predicting student placements based on academic and non-academic factors is an increasingly valuable tool for educational institutions.This paper presents a machine learning-based approach to analyze and predict the likelihood of campus placement for students.By examining a variety of features such as academic performance, skill sets, internships, and extracurricular activities, predictive models are trained to assess student readiness and employability.The study compares multiple algorithms, including Logistic Regression, Random Forest, and Support Vector Machines, to determine the most accurate and reliable method for placement prediction.Our findings suggest that machine learning models, particularly ensemble methods, can significantly improve the precision of placement forecasts and provide actionable insights for students and administrators to enhance placement outcomes.