GitHub / vahadruya / Capstone_Classification_Cardiovascular_Risk_Prediction

This project explores the Framingham Heart disease dataset with the objective to predict its risk in 10 years. Various methods for handling missing values and outliers are explored as iterations. After analysing the dataset, important and necessary features are selected. Seven ML models are implemented, with evaluation on the basis of Test Recall.

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Topics: chi-squared-test, classification, decision-trees, heart-disease-prediction, knn-classifier, knn-imputation, pandas, python, random-forest-classifier, recall-score, regression-imputation, shap, shapiro, sklearn, smote, t-test, trimming, winsorization, xgboost-classifier

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