This project develops a Random Forest model to predict heart disease using the UCI Heart Disease dataset. It evaluates patient attributes like age, cholesterol levels, and chest pain type to determine the likelihood of heart disease. The model aims to provide an accurate, data-driven approach for early detection and prevention of heart disease.
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This project develops a Random Forest model to predict heart disease using the UCI Heart Disease dataset. It evaluates patient attributes like age, cholesterol levels, and chest pain type to determine the likelihood of heart disease. The model aims to provide an accurate, data-driven approach for early detection and prevention of heart disease.
georgejr45/RandomForest
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This project develops a Random Forest model to predict heart disease using the UCI Heart Disease dataset. It evaluates patient attributes like age, cholesterol levels, and chest pain type to determine the likelihood of heart disease. The model aims to provide an accurate, data-driven approach for early detection and prevention of heart disease.
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