This data set dates from 1988 and consists of four databases: Cleveland, Hungary, Switzerland, and Long Beach V. It contains 76 attributes, including the predicted attribute, but all published experiments refer to using a subset of 14 of them. The target field refers to the presence of heart disease in the patient.
0 = doesn't have heart disease
1 = has heart disease
data -> Contains the dataset of this project.
notebooks -> Contains two notebooks. One is for analysis another is for model evaluation.
src -> Contains all python files.
The dataset was pretty straight forward. Didn't need much preprocessing. Feature Engineeing, Feature Selection, Feature Scaling lead to overfitting. So, I avoided those steps. Dataset was divided into train and dev set. Trained the model on train set and check overfitting on dev set.
Random Forest algorithm has highest score.
train score: 0.987
dev score:0.976