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Used CDC dataset for heart attack detection in patients. Balanced the dataset using SMOTE and Borderline SMOTE and used feature selection and machine learning to create different models and compared them based on metrics such as F-1 score, ROC AUC, MCC, and accuracy.

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Averaged Results using Excel

Averaged Results using Excel

 
 

CS 699 Project Code & Data Sets

CS 699 Project Code & Data Sets

 
 

CS 699 Project Osama, Muhammad & Oruganti, Sravani

CS 699 Project Osama, Muhammad & Oruganti, Sravani

 
 

.Rhistory

.Rhistory

 
 

CS 699 Project Presentation Osama, Muhammad & Oruganti, Sravani.pptx

CS 699 Project Presentation Osama, Muhammad & Oruganti, Sravani.pptx

 
 

CS 699 Project Presentation.pptx

CS 699 Project Presentation.pptx

 
 

CS 699 Project Report Osama, Muhammad & Oruganti, Sravani.docx

CS 699 Project Report Osama, Muhammad & Oruganti, Sravani.docx

 
 

CS 699 Project Report Osama, Muhammad & Oruganti, Sravani.pdf

CS 699 Project Report Osama, Muhammad & Oruganti, Sravani.pdf

 
 

CS 699 Project.R

CS 699 Project.R

 
 

Full Clean Data.csv

Full Clean Data.csv

 
 

Heart Attack Detection Dataset.csv

Heart Attack Detection Dataset.csv

 
 

Osama_Muhammad, Oruganti_Sravani CS 699 Project Proposal.docx

Osama_Muhammad, Oruganti_Sravani CS 699 Project Proposal.docx

 
 

Osama_Muhammad, Oruganti_Sravani CS 699 Project Proposal.pdf

Osama_Muhammad, Oruganti_Sravani CS 699 Project Proposal.pdf

 
 

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Used CDC dataset for heart attack detection in patients. Balanced the dataset using SMOTE and Borderline SMOTE and used feature selection and machine learning to create different models and compared them based on metrics such as F-1 score, ROC AUC, MCC, and accuracy.

Topics

python machine-learning feature-selection naive-bayes-classifier logistic-regression balancing adaboost decision-trees svm-classifier smote random-forest-classifier recursive-feature-elimination forward-selection borderline-smote
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