Cardiac Arrhythmia Multi-Class Classification
This Analysis has been conducted to classify patients into one of the sixteen subclasses, among which one class represents absence of disease and the other fifteen classes represent electrocardiogram records of various subtypes of arrhythmias.
The Dataset contains nearly 280 variables, I have followed the below steps to start my analysis
Started from feature selection technique to identify the important variables.
Exploratory analysis and simple machine learning models like KNN, logistic regression, random forest, decision tree, linear & kernelized SVM and compared the precision and recall of each of the above-mentioned models
Bagging and boosting to check and improve the model performance
After performing the PCA, best model is achieved with 75 % accuracy