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Comparison of machine learning models for prediction of diabetes.

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Name		Name	Last commit message	Last commit date
Latest commit History 18 Commits
README.md		README.md
diabetes.csv		diabetes.csv
diabholdout.m		diabholdout.m
diabkfold.m		diabkfold.m
hyperparameter.m		hyperparameter.m
report.pdf		report.pdf
testingdataDT.csv		testingdataDT.csv
testingdataNB.csv		testingdataNB.csv

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Machine-Learning-Project

File Name and Description :

diabetes.csv - Original data set.
diabholdout.m - MATLAB script of descriptive statistics,EDA,partition of original data set into train and test sets (holdout method), Decision Tree model training and testing, Naive Bayes model training and testing.
diabkfold.m - MATLAB script of descriptive statistics,EDA,partition of original data set into train and test sets (kfold method), Decision Tree model training and testing, Naive Bayes model training and testing.
hyperparameter.m - MATLAB script of optimization of the models.
testingdataDT.csv - Excel file of test data used for Decision tree model.
testingdataNB.csv - Excel file of test data used foe Naive Bayes model.
report.pdf - Project report.

Sequence for running the codes :

Start by opening and running each section individually of the file diabholdout.m then run hyperparameter.m section by section for optimization.
After this for kfold method run file kfold.m then run hyperparameter.m for optimization.