Diabetes is a chronic disease with the potential to cause a worldwide health care crisis. Various traditional methods, based on physical and chemical tests, are available for diagnosing diabetes. However, early prediction of diabetes is quite challenging task for medical practitioners due to complex interdependence on various factors as diabetes affects human organs such as kidney, eye, heart, nerves, foot etc. Data science methods have the potential to benefit other scientific fields by shedding new light on common questions. One such task is to help make predictions on medical data. Machine learning is an emerging scientific field in data science dealing with the ways in which machines learn from experience. The aim of this project is to develop a system which can perform early prediction of diabetes for a patient with a higher accuracy by combining the results of different machine learning techniques. This project aims to predict diabetes via different supervised machine learning methods including:SVM, Logistic regression, k-neighbors classifier, Naïve Bayes, decision tree and random forest. This project also aims to propose an effective technique for earlier detection of the diabetes disease.