%% Homework
% This dataset is originally from the National Institute of Diabetes and Digestive and Kidney Diseases. As a chronic disease, diabetes mellitus has emerged as a worldwide epidemic. The objective of the dataset is to diagnostically predict whether a patient has diabetes and to develop a machine learning system for classification of individuals  based on certain simplistic diagnostic measurements included in the dataset. The features are number of times pregnancies, plasma glucose concentration, diabetes pedigree function, triceps skin fold thickness (mm), diastolic blood pressure (mmHg), 2-h serum insulin (mU/mL), body mass index (kg/m 2 ) and age in years.
% Several constraints were placed on the selection of these instances from a larger database. In particular, all patients here are females  at least 21 years old of Pima Indian heritage - the Indian tribes in Mexico and Arizona areas.
% From the data set in the (.csv), one can observe the various variable names. Each of these are independentl medical predictors of diabetes, The target variable is Outcome which takes on the value of 0 (non-diabetic) and 1 (diabetic).
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% This dataset is provided to you in the file diabetes.csv
% Perform the pre-processing steps including:
% Removing missing target - data
% Filling missing feature values
% Removing Outliers
% Scaling
% Train the following three classifiers:
% Nearest Neighbor
% Support Vector Machines
% Decision Tree
% Compare the Performance of the three classifiers using the AUC Metric.
% Finally, use the Classification Learner to identify the optimal model for this dataset.
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