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A sample project that aims to train a model to perform classification for the possibility of cardiovascular disease risks. The exploratory data analysis is performed through descriptive statistics and correlation analysis. Subsequently, a prediction model is developed employing Logistic Regression, Decision Tree, and Random Forest Classification algorithms. Model performance is evaluated on both training and testing datasets, with Random Forest Classification demonstrating the highest accuracy. The results are visualized and discussed, concluding that Random Forest Classification outperforms other models in predicting the 10-year risk of coronary heart disease.
Full Changelog: https://github.com/iamwatchdogs/Cardiovascular-Risk-Prediction/commits/v1.0.0