Data mining techniques are useful to extract the useful information and to support in the decision making process. There are too many application in the educational domain where we can apply the data mining. Right now data mining in Educational domain is rapidly developing technique. In this research paper we analyse the student’s result of every semester using data mining techniques. Data mining supports the too many techniques but here in the analysis we are using the various classification algorithm of data mining techniques. Here in this research analysis we worked on two model. Model A uses the dataset that contains all the student performance parameters and data mining classification techniques and generated the result based on Accuracy and Error Rate of the classifiers and Model B uses the dataset contains only statistically proved highly affected parameters on student performance and applied data mining techniques on this data sets and generate the results based on Accuracy and Error rate of the classifiers. This research work compare the result of both the model and check that which model is best. The comparison is done using the measurement of accuracy and measurements of Error Rate. This research work also shows that which algorithm is most suitable for predicting the performance of the students among the selected algorithms. The analysis work is done by considering various types of algorithm like decision tree algorithm, rule based algorithm, Bayesian algorithm and function based algorithms. This generic novel approach can be extended to other disciplines as well.

classification, error rate, data set, data mining, prediction.

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