The diabetes is one of lethal disease in the world. It is additional a inventor of various varieties of disorders for example : coronary failure, blindness, urinary organ diseases etc. In such case the patient is required to visit a diagnostic center, to get their reports after consultation. Due to every time they have to invest their time and currency. But with the growth of machine learning methods we have got the flexibility to search out an answer to the current issue, we have got advanced system mistreatment information processing that has the ability to forecast whether the patient has polygenic illness or not. Furthermore, forecasting the sickness initially ends up in providing the patients before it begins vital. Information withdrawal has the flexibility to remove unseen data from a large quantity of diabetes associated information. The aim of this analysis is to develop a system which might predict the diabetic risk level of a patient with a better accuracy. Model development is based on categorization methods as Logistics Regression, Decision Tree, KNN, Random Forest, SVM, Naïve Bayes, Linear Regression algorithms. For Logistics Regression , the models give accuracy of 73.59%, For Decision Tree 70.56%, For KNN 70.13%, For Random Forest 75.76%, For SVM 74.89%, For Naïve Bayes 74.46%, and 73.16% for Linear Regression.

Machine Learning, Logistic Regression, Decision Tree, K-Nearest Neighbours, Random Forest, Support Vector Machine, Naïve Bayes, Linear Regression, Dataset.