Diabetes is an illness caused because of high glucose level in a human body. Diabetes should not be ignored if it is untreated then Diabetes may cause some major issues in a person like: heart related problems, kidney problem, blood pressure, eye damage and it can also affects other organs of human body. Diabetes can be controlled if it is predicted earlier. To achieve this goal this project work we will do early prediction of Diabetes in a human body or a patient for a higher accuracy through applying, Various Machine Learning Techniques. Machine learning techniques Provide better result for prediction by constructing models from datasets collected from patients. In this work we will use Machine Learning Classification and ensemble techniques on a dataset to predict diabetes.
1.Logistic Regression
2.Support Vector Machine
3.KNN
4.Random Forest
5.Naive Bayes
6.Gradient Boosting
By using Supervised Machine algorithms like Logistic Regression, Support Vector Machine, KNN, Random Forest, Naives Bayes and Gradient Boosting. Random Forest gives High accuracy than the other algorithms.
It gives 98% Accuracy and ROC 97%.
