Nutrient facts analysis using supervised learning approaches

Abstract

A healthy lifestyle in people is achieved by having balanced and nutritional food. In today's world we do not know with absolute certainty what foods can be consumed and what cannot be consumed, that is, we do not know for sure what foods have good nutritional value and what foods do not. The nutritional facts label is printed on food products all over the world and they are represented using a similar structure. These nutritional facts give data about some of the major nutrients present in the food product such as carbohydrates, protein and so on. These nutrition fact labels are not easily understood by common people. People who are careful about their diet such as those who exercise and diet regularly, trainers, and nutritionists may understand these nutritional facts, but not the common people. To make this information accessible in an easier way by classifying these food products into five levels of healthiness ranging from very healthy to very dangerous is the aim of this project work. This is done by a sequential process of data retrieval, data cleaning, data labeling and supervised learning.