Data mining techniques are being extensively used in almost every field to gain insights into the large data that is being generated worldwide. These techniques are also being used in analysing the student’s performance in several educational institutions. Some of the student data of a particular college was collected with information about their academic performances and individuals’ skills such as their aptitude level and communication skills. This data was gathered in order to analyse and predict the placement of the students by applying classification techniques such as decision tree algorithm (J48), K-Nearest Neighbour (IBk) and Naïve Bayes using one of the Data Mining tools known as WEKA. The rules were generated using the decision tree algorithm which helped us in visualizing the decision tree using the above dataset. The accuracy of all the three classifier models was compared and it was shown that the highest accuracy was shown by KNN classifier (IBk in Weka) followed by J48 classifier and Naïve Bayes classifier. This system is thus much reliable and can be used to predict if the student can be placed or not.

Data Mining, Classification, Decision Tree, Naive Bayes, Prediction

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