Supervised Learning

1.Supervised_Module2_Diabetes.ipynb Using the K-Nearest Neighbour algorithm to classify patients into Diabetic(1) and Non- Diabetic(0) based on a number of health attributes such as Glucose content, Blood Pressure, BMI etc. The diabetes.csv dataset is available online. I also demonstrate the confusion matrix, precision, recall and error rate to visualize overall accuracy. An accuracy of 80% is achieved by KNN algorithm for this dataset.