AdaBoost Classifier.

The AdaBoost Classifier was utilized to predict the values in the "Healthy" column of a dataset consisting of four categorical columns ('Food preference', 'Smoker?', 'Living in?', 'Any hereditary condition?') and eleven numerical columns ('Specific ailments', 'Age', 'BMI', 'Follow Diet', 'Physical activity', 'Regular sleeping hours', 'Alcohol consumption', 'Social interaction', 'Taking supplements', 'Mental health management', 'Illness count last year'). To handle missing values, the columns ('Follow Diet', 'Physical activity', 'Regular sleeping hours', 'Alcohol consumption', 'Social interaction', 'Taking supplements', 'Mental health management', 'Illness count last year') were imputed with the median using a simple imputer, while the remaining columns were marked as 'missing'. The AdaBoost Classifier is an ensemble learning method that combines multiple weak learners (base estimators) to create a strong predictive model. It sequentially fits the base estimators to the dataset, with each subsequent estimator placing more emphasis on the instances that were misclassified by the previous estimators. This approach helps to improve the model's overall accuracy and robustness. After training the AdaBoost Classifier on the dataset, the model's performance was evaluated using appropriate metrics such as accuracy, precision, recall, or F1 score. These metrics provide insights into the classifier's ability to accurately predict the "Healthy" values. The predictions made by the AdaBoost Classifier were saved in a CSV file named "submission.csv." This file contains the predicted values for the "Healthy" column based on the input data and the trained AdaBoost Classifier model.