Smoking behavior prediction

Applies various machine learning algorithms (Decision Tree, KNN, Naive Bayes, SVM) to predict whether a person is a smoker based on health-related features. It covers data preprocessing, visualization, model comparison, and evaluation. Results show that a scaled SVM achieves the highest accuracy (~75.8%), though with higher computational cost.