Student Placement Prediction Using Machine Learning

Abstract

Placement of scholars is one in every of the vital activities in academic establishments. Admission and name of establishments primarily depends on placements. The main Objective of this paper is 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. This system presents a recommendation  system that predicts whether the current student will be placed or not, if the student is placed the company is also predicted based on the data of previously placed students. Here we use two different machine learning classification algorithms, namely Naive Bayes Classifier and K-Nearest Neighbors [KNN] algorithm to predict the results and we then compare the efficiency of the algorithms, which is based on the dataset. This model helps the position cell at intervals a corporation to spot the potential students and concentrate to and improve their technical and social skills