An ML model for Hypertension prediciton using random forest classifier, XGBoost classifier and SMOTE.
ABSTRACT
This project predicts hypertension risk using health features like Age, Gender, BMI, and Genetic Disorders. After label encoding, scaling, and balancing with SMOTE, Random Forest and XGBoost models are trained and evaluated to identify the best predictor.
ALGORITHMS USED
•Random Forest Classifier
•XGBoost Classifier (Extreme Gradient Boosting)
•SMOTE
DATASET USED
Personalized Medication Dataset