This project implements a classification model to predict whether an individual is healthy based on various features like physical fitness, mindfulness, and other lifestyle factors. It evaluates classifiers such as SVM, Random Forest, and Gradient Boosting.
Data Preprocessing: Load and clean the dataset, apply encoding. SMOTE Oversampling: Balance the dataset for better model performance. Model Training: Train and tune classifiers (SVM, Random Forest, Gradient Boosting) using GridSearchCV. Evaluation: Select the best classifier based on accuracy.