Diabetes Mellitus (TY2) has secured the status of a global pandemic. Thus, the diagnosis of the disease at an early stage plays a very significant role as the early prediction of the disease will minimize the health risks associated with the disease. This projects aims to predict whether a person is diabetic or not via six different classification algorithms namely Support Vector Machine(SVC), K- Nearest Neighbour(Knn), Decision Tree, Naive Bayes, Logistic Regression and Random Forest. Moreover, early prediction of the diabetes disease with a higher accuracy can be found using various machine learning techniques.