Education is an essential element for the development of a country. Lack of knowledge in higher educational system could prevent system management to achieve quality in education. Data mining methodology can help associating this knowledge gaps in higher education system. A better student model yields better instruction, which leads to improved learning. More accurate skill diagnosis leads to better prediction of what a student knows which provides better assessment. Better assessment leads to more efficient learning overall. The main objectives of data mining in practice tend to be prediction and description. Predicting performance involves variables like attendance, IAT marks and assignment grades etc. in the student database to predict the unknown values.
Processing: As the dataset is obtain from the above steps contain many unnecessary information which one need to be removed for making proper operation. Here data need to be read as per the algorithm such as the arrangement of the data in form of matrix is required.
K-Anonymity: Here some specific data like age, salary, postal code, etc. are to be hidden which directly specify the user relation with the transaction. This is done by creating the range of particular values and replacing that value with that range, so that individual privacy of the user is also taken care of in this work. For generating the range, random function is used that generates number in fix range then replace original information with this range.
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