Diabetes is considered as one of the deadliest and chronic diseases which causes an increase in blood sugar. According to the growing morbidity in recent years, in 2040, the world’s diabetic patients will reach 642 million, which means that one in ten adults in the future has diabetes. There is no doubt that this alarming figure needs great attention. The remarkable advances in biotechnology and health sciences have led to a significant production of data, such as high throughput genetic data and clinical information, generated from large Electronic Health Records (EHRs). To this end, application of machine learning and data mining methods is vital and indispensable in efforts to transform intelligently all available information into valuable knowledge. The motive of this study is to design a model which can predict the likelihood of diabetes in patients with maximum accuracy. This project implements four linear model and one deep learning model: Logistic regression Naïve Bayes, Support Vector Machine, K-Nearest Neighbors and Multilayer Perceptron Neural network to investigate performance on diabetes disease dataset obtained from UCI data repository. For the training purpose, 768 datasets have been used, among which 500 are non-diabetic and 268 are diabetic. The performances of all the algorithms are evaluated on the basis of accuracy. In addition to the comparison of the algorithms, each algorithm has been integrated into a prediction engine and exposed over an API. As the results show, Multi-Layer Perceptron Neural Network outperforms other linear models however K-Nearest Neighbors gives identical results with less computing overhead. Performance improvements could also be achieved by using complex deep learning methods