  The four essential chambers of one's heart that lie in the thoracic cavity
are crucial for one's survival, yet ironically prove to be the most vulnerable.
Cardiovascular disease (CVD) also commonly referred to as heart disease has
steadily grown to the leading cause of death amongst humans over the past few
decades. Taking this concerning statistic into consideration, it is evident
that patients suffering from CVDs need a quick and correct diagnosis in order
to facilitate early treatment to lessen the chances of fatality. This paper
attempts to utilize the data provided to train classification models such as
Logistic Regression, K Nearest Neighbors, Support Vector Machine, Decision
Tree, Gaussian Naive Bayes, Random Forest, and Multi-Layer Perceptron
(Artificial Neural Network) and eventually using a soft voting ensemble
technique in order to attain as many correct diagnoses as possible.
