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Diabetes mellitus is very serious health problem today. If the disease is not identified and treated it can cause very dangerous health hazards. It is very important to predict the diabetic condition to control it through proper medical treatment. There are various hardware and software methods used to predict and classify the data. Various classification algorithms are used to classify the status of a person that whether a person is diabetic or not using various physical and biochemical characteristics. Different methods have different techniques and varying accuracy. In the present work a method for classifying the diabetic data is developed. It is the KNN classifier with six neighbor instance to classify any data record. KNN is lazy learning algorithm which classifies the data based on the majority of neighbors. For the better and accurate classification purpose normalization technique is used to normalize the data …