Educational data mining has gained importance for discovering the useful information from the student databases. It is observed that there is a lack of performance of the students during campus selection in technical institutions. Hence the problem highlighted in this research work is: “What factors are responsible for placement of some students but why not others during campus selection of technical institutions?” The objective of this research work is related to the prediction and discovery of the factors for student placement using the data mining techniques and tool. The methodology used in this research work involves four main stages to achieve the required objectives. They are Data Collection, Pre-processing, Classification and Interpretation of Result. The Classification algorithms used in this research paper include decision tree, Naive Bayes, Neural Network (Multilayer perceptron) and Sequential Minimal Optimisation. It has been found that Naive Bayes algorithm works best in student placement prediction with maximum accuracy. The identification of attributes is done using output decision tree model. After such findings, a classification system model is proposed which depicts the stages of pre-processing, attribute selection, classification, factor identification, factor improvement and placement prediction. It may also be applied at any institute where placement prediction is required before-hand to increase the chances of campus selection irrespective of courses. The classification model can be applied to the problems related to student placement at technical institutions.

Educational Data Mining, Placement chance prediction, Classification Algorithms, Attribute selection, Student Performance.

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