Ensemble method to predict impact of student intelligent quotient and academic achievement on placement

The study's aim is to see how academic achievement and student Intelligence Quotient influence placement. This paper will attempt to predict whether a student's intelligence quotient or academic score plays a significant role in placement. On a dataset of 193 students, we used a machine learning algorithm to compare the impact of student intelligence, behavior, and academic achievement on placement. We have used a Voting Classifier architecture to predict and classify the probability of a student being placed or not. The motivating force behind this research was to figure out why a group of students scoring the same marks in the same branch studying under the supervision of the same faculty are not able to fulfill the demands of an organization in order to be employed. The aim of this research was to combine conceptually different machine learning classifiers and predict the probability of a student being hired using a majority vote or the average expected probabilities. A classifier like this can be useful for balancing out the weaknesses of a group of models that are all performing well. Experiments show that student intelligence and attitude play a significant role in the recruiting process.