Predicting Stroke Using Various Classification Methods

A tétel rövid nézete
dc.contributor.advisorBertók, Csanád
dc.contributor.advisorNagy, Dávid
dc.contributor.authorSingh, Veer
dc.contributor.departmentDE--Informatikai Karhu_HU
dc.date.accessioned2021-11-15T10:33:22Z
dc.date.available2021-11-15T10:33:22Z
dc.date.created2021-11-15
dc.description.abstractThis thesis tries to create predictive models to predict stroke. We explore three supervised machine learning algorithms; K-Nearest Neighbors, Support Vector Machine, and Random Forest to create these predictive models. The dataset used contains over 5000 instances of patient records with features like age, BMI, marital status, etc. Before training the models, the data was researched using exploratory data analysis methods and then fine-tuned using feature engineering. Finally, the dataset was encoded to allow it to be read by the algorithms. Each model was further improved with hyperparameter tuning, achieving F1-scores of above 96%.hu_HU
dc.description.courseComputer Science Engineeringhu_HU
dc.description.degreeBSc/BAhu_HU
dc.format.extent57hu_HU
dc.identifier.urihttp://hdl.handle.net/2437/324635
dc.language.isoenhu_HU
dc.subjectMachine Learninghu_HU
dc.subjectClassificationhu_HU
dc.subjectSupervised Learninghu_HU
dc.subjectStrokehu_HU
dc.subject.dspaceDEENK Témalista::Informatikahu_HU
dc.titlePredicting Stroke Using Various Classification Methodshu_HU
Fájlok
Gyűjtemények
Theses (IK)