ABSTRACT
The practice that examines huge datasets to find new information, new trends, and hidden patterns is called data mining. A decision tree is made by analyzing these patterns which can be used to generate some kinds of results. Data-mining technique is used in the proposed system, which helps to predict the placement probability of students. Classification in machine learning responds to classifying new data or observation on the basis of training dataset. For the purpose of data mining, various well-known algorithms are used such as K-nearest neighbor classifier, naïve Bayes algorithm, and decision tree algorithm. But among all these algorithms, C.4.5 algorithm, which is an extended version of ID3 algorithm, is used because it provides more accurate results compared to the latter ones. Also, efficiency is also increased. It also considers more number of attributes while making a decision tree, which helps to improve the accuracy. Campus placements by corporate companies are a great deal of ordeal for students as well as recruiters. It is very important to select the right candidate amongst the pool of students. While selecting the candidates, it should be entirely dependent on merit of students, which includes not only the academic performance but also their overall capability. In this procedure, merit should not be thrashed at any condition. The proposed system helps to recognize this merit. Even from the recruiter’s point of view, resources and amount of time used in this procedure will also be reduced. These resources can be used for another fruitful purpose, which will help in the growth of the company. Here, by applying C4.5 decision tree algorithm, the results become thoroughly analytical and easily persuadable. The proposed system is dependent on the past data, which will be used to train the algorithm. Then, it will construct a decision tree which will be used to make nearly accurate prediction on the present data.
