Student performance predictor using multiclass support vector classification algorithm

Educational data mining provides the process of applying different data mining tools and techniques to analyze and visualize the data of an institution and can be used to discover a unique pattern of students' academic performance. Secondary schools are increasing rapidly from past years and progress of the institution can be measured based on student's success and failure rates. Failure rates can be measured in terms of a core subject such as mathematics which has been considered in this proposed system. Real data was collected using school reports and questionnaire method by the Portugal school which has been used for the project. The students will be classified according to the grades assigned for the range of marks scored by them. This involves classifying the students into five levels of grading system starting from grade `A' to grade `F' where grade `A' represents the student getting the highest marks, grade `B' being the second highest, grade `C' being third, grade `D' being the fourth and the grade `F' which implies that the student has failed. To carry out this type of classification many machine learning algorithms can be used to implement it. Comparison was made between the algorithms like Multiclass Support Vector Machine and Neural Networks using the Weka tool. Based on the analysis carried out Multiclass Support Vector Machine showed prominent accuracy. The Multiclass Support Vector Machine is implemented on the basis of one-to-rest strategy use of class labels which is primarily an extension of linear Support Vector Machine. In order to get appropriate results in terms of the accuracy of the model, parameters like `C' and `Gamma' is tuned while implementing Multiclass Support Vector Machine. The result gives a good predictive accuracy based on the grades that are provided by the school. The accuracy of prediction made by Support Vector Machine Classifier is determined by using K-fold cross validation, according to which the accuracy is 89%.