An Improved Prediction Model for the Placement of the Students Considering Various Job Aspects

As the job market transforms, the placement of the students become very important with regard to career progression on an individual level as well as an institutional level. In the traditional placement models, the primary focus remains upon the academic grades while assigning very low or negligible importance to aspects like internships, soft skills, additional activities, industry certifications, and even job preferences. This work applies machine learning methods towards the development of a model designed to predict successful student placement achievement based on multiple features. This research studies various predictive models, such as Random Forest, Decision Tree, and XGBoost, and their efficiency in predicting placement achievement. Predictive placement models based on these algorithms are designed and tested to assess the pros and cons of each model in comparison to a specific placement achievement. Additionally, the differing models are evaluated based on interpretability, computation time, and efficiency on the considered data. In this case, Decision Tree provides improved clarity while overfitting is reduced by enhanced robustness of Random Forest. XGBoost surpasses both these approaches by XGBoost, which is the optimal approach for this problem.. The model is also improved by carrying out integration, normalization, and parameter optimization on GridSearchCV. Hereby, this research provide AI-driven adaptive strategies to enhance the placement by facilitating better alignment with academic professional and industry professionals in order to improve students’ employability .