GitHub - mosama1994/Diabetes-Detection-using-Machine-Learning: Diabetes detection in patients using different machine learning techniques and comparing the algorithms based on confusion matrix and other metrics.

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Diabetes detection in patients using different machine learning techniques and comparing the algorithms based on confusion matrix and other metrics.

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Name		Name	Last commit message	Last commit date
Latest commit History 2 Commits
Accuracy VS Depth for Decision Tree Classifier.jpg		Accuracy VS Depth for Decision Tree Classifier.jpg
AdaBoost with Base Estimator = Logistic Regression.jpg		AdaBoost with Base Estimator = Logistic Regression.jpg
AdaBoost with Base Estimator = Naive Bayesian.jpg		AdaBoost with Base Estimator = Naive Bayesian.jpg
AdaBoost with Base Estimator = Random Forest Classifier.jpg		AdaBoost with Base Estimator = Random Forest Classifier.jpg
CS_777_Assignment_4.py		CS_777_Assignment_4.py
Correlation Matrix.jpg		Correlation Matrix.jpg
Diabetes CDC BRFSS 2015.csv		Diabetes CDC BRFSS 2015.csv
Diabetes Detection Using Machine Learning.pptx		Diabetes Detection Using Machine Learning.pptx
Inertia VS Number of clusters.jpg		Inertia VS Number of clusters.jpg
KNN Accuracy VS K for P = 1.5.jpg		KNN Accuracy VS K for P = 1.5.jpg
KNN Accuracy VS K for P = 1.jpg		KNN Accuracy VS K for P = 1.jpg
KNN Accuracy VS K for P = 2.jpg		KNN Accuracy VS K for P = 2.jpg
KNN Accuracy VS K for P = 3.jpg		KNN Accuracy VS K for P = 3.jpg
Muhammad Osama U41261655 CS 677 Semester Project Description.docx		Muhammad Osama U41261655 CS 677 Semester Project Description.docx
Muhammad Osama U41261655 CS 677 Semester Project.py		Muhammad Osama U41261655 CS 677 Semester Project.py
Number of clusters vs Inertia.jpg		Number of clusters vs Inertia.jpg
Pair Plot.jpg		Pair Plot.jpg
Random Forest Accuracy VS Max Depth & Number of Estimators.jpg		Random Forest Accuracy VS Max Depth & Number of Estimators.jpg