Journal of Engineering and Applied Sciences
ISSN: Online 1818-7803ISSN: Print 1816-949x
Abstract
Engineering students are dubious about what they need to pursue after graduation. With extensive options available, starting from campus recruitments 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 beginning of engineering and take action to bridge this gap for a far better future. Placement of students is one of the vital activities in academic establishments. Admission primarily depends on placements. Admission is directly proportional to the offer letters received by an institute. Hence all institutions strive to strengthen the placement department. Students studying in engineering colleges feel the difficulty to understand where they substitute comparison to others and what quite a placement they might get. The training and placement offices are available to an image when a student enters the final year but they’re of no use to a student planning for future studies. Prediction about the student’s performance is an integral part of an education system because the overall growth of the scholar is directly proportional to the success rate of the scholars in their examinations and extra-curricular activities. Therefore, there are many situations where the performance of the scholar must be predicted for instance in identifying weak performing students and taking actions for his or her betterment. The students do not have any platform to see their current position and repose on their strengths. The platforms currently available haven’t been trained on real and complete data sets and don’t learn from their wrong predictions which reduces the accuracy within the future. To realize far better efficiency and a system that determines with every wrong prediction it’s made, so it uses algorithms like Logistic Regression, Support Vector Machine (SVM), K-Nearest Neighbors (KNN) which can cause endless accuracy growth. The model is going to train on a real data set and a massive number of qualitative as well as quantitative parameters are going to consider. This study aims to study the previous year’s student’s data and predict the placement possibilities of current students and aids in increasing the situation percentage of the institutions. This study presents a recommendation system that predicts whether the present student is going to be placed or not with a percentage value. This study helps the placement cell at intervals to identify potential students and concentrate on and improve their technical and social skills.
How to cite this article:
Varul and Shubham Tiwari. Recommendation System for Predicting the Placement Percentage for an Educational Institute.
DOI: https://doi.org/10.36478/jeasci.2020.2817.2826
URL: https://www.makhillpublications.co/view-article/1816-949x/jeasci.2020.2817.2826