Diabetic is a disease that occurred when the level of blood glucose is higher than usual, which is also known as hyperglycemia. When the human body is incapable of producing enough insulin(a hormone that produces glucose from food), then this situation leads to diabetes. The rapid increase of this disease makes the researchers work much harder in this area to build a model for diagnosing diabetes efficiently. As in healthcare, the availability of data is high, so it is easy to extract information from those data to diagnose disease and develop a new model for better results. This paper aims to introduce a model that can predict diabetes efficiently with the help of machine learning algorithms. Here logistic regression, SVM, and k nearest neighbor algorithms have been used for the classification of diabetics. After data preprocessing and training, those algorithms gave a good result. Logistic regression provided the best accuracy of 83% for test data. Also, SVM and knn both performed well and showed an accuracy of 82% and 79%, respectively. The proposed model has demonstrated improved results compared with previous work.