Vitamin Deficiency and Food Recommendation Using Machine Learning

Abstract

Abstract: This project’s unique dataset is created from a of both high and low levels of a few vitamins (a, b, c, d, e, and k). Qualities are divided into Vitamin-related regular and irregular while the As usual, labels are divided into o and 1as abnormal. Another dataset predicts illnesses caused by vitamin shortages by utilizing a different that produces based based on deficiencies in multiple vitamins additionally food recommendations depending Whatever one does not own. KNN and naïve are among the classifier methods employed. bayes classifier, support vector machine, voting classifier random forest. The precision of every algorithm is evaluated after which the best performing algorithm is used to predict. Flask web application shows the prediction; it can identify vitamin deficiencies; it forecasts disease types; and suggest different meal combinations too.