Improving classification algorithm on education dataset using hyperparameter tuning

In this paper, researchers propose a classification method for any institution's campus placement possibility using Placement Data Full Class for campus recruitment dataset. Researchers attempt to study the supervised learning classification algorithms such Logistic Regression, Support Vector Classifier (SVC), K-Nearest Neighbors (KNN), Gaussian Naive Bayes, Decision Tree, Random Forest, Gradient Boosting, and Linear Discriminant Analysis (LDA). Hyperparameter optimization also used to optimize the supervised algorithms for better results. Experimental results have found that by using hyperparameter tuning in Linear Discriminant Analysis (LDA), it can increase the accuracy performance results, and also given a better result compared to other algorithms.