Risk of Future Coronary Heart Disease Worksheet

To predict the 10-year risk of future coronary heart disease (CHD) in patients

IN PYTHON

The dataset provided information on over 4,000 patients and included 15 attributes, each representing a potential risk factor for CHD. These attributes included demographic, behavioral, and medical risk factors.Task :

“To predict the 10-year risk of future coronary heart disease (CHD) in patients”

• Use logistic regression and Random forest
• Use binning and provide examples
• Which features are really important
• Use Upsampling and downsampling for unbalanced data . And which is the best method and why?
• Provide cross-validation and performance evaluation metrics specific to imbalanced datasets, to ensure the model’s effectiveness and generalizability.