Placement prediction system

Abstract

Engineering students are skeptical about what they want to pursue after graduation. With wide options available, ranging from campus recruitment to Masters, students are perplexed, adding factors like salaries and different job opportunities makes it even worse. There aren’t any reliable platforms where a student can predict the outcomes from the start of engineering and take actions to bridge this gap for a better future. Students studying in Engineering colleges feel the exigency to know where they stand in comparison to others, and what kind of placement they would get. The training and placement offices come in the picture when a student enters final year, but they are of no use to a student planning for future studies.Placement of students is one of the most important objectives of an educational institution. Institutions make great efforts to achieve placements for their students.The objective is to predict the students getting placed for the current year by analyzing the data collected from previous years students.Prediction about the student’s performance is an integral part of an education system, as the overall growth of the student is directly proportional to the success rate of the students in their examinations and extracurricular activities. Therefore, there are many situations where the performance of the student needs to be predicted, for example, in identifying weak performing students and taking actions for their betterment. The students have no platform to check their current position and build on their strengths. The platforms currently available, have not been trained on real and complete data sets, and do not learn from their wrong predictions which reduces the accuracy, in the long term. We aim to develop one. To ensure effective results, the model will be trained on a real data set and a vast number of qualitative as well as quantitative parameters will be considered.This model is proposed with an algorithm to predict the same. The data has been collected by the institution for which prediction is going to be done and by applying suitable data pre-processing techniques or to analyze previous year’s student’s historical data and predict placement possibilities of current students and aids to increase the placement percentage of the institutions.