STUDENT PLACEMENT PREDICTION

Abstract

In an educational institution, the most important objective is the placement of students. For each and every student, the placement part is a very important one in college life because for some sets of students it is the future. The prediction of students will not be 100% accurate but it depends how the students perform in every part of the placement. So, to predict the placement package chance of current students, we can analyze the previous year’s students data. The data has been collected from the institution and certain pre-processing techniques are applied to the models. Different algorithms have different accuracy. Depending on the type of issue and dataset to be solved, different algorithms have varied levels of accuracy. As a result, we decided to assess the accuracy levels of three methods, namely Logistic Regression, Decision Tree Classifier, and Random Forest Classifier, with respect to our challenge and dataset. The efficiency/accuracy of each model is visualized and tested and based on the performance analysis, the best model results are declared.