Nutrition Recommendation System for Sports Persons using Random Forest Algorithm

Recommendation systems gained traction in all spheres of human endeavor. When looking for any kind of product—from videos and movies to reading materials and news—everyone looks for recommendations. Their influence on food and dish recommendations is likewise growing daily. Food suggestions for groups are more difficult than for individuals because individual preferences need to be considered. Good meals should also be suggested by the system because poor diets might lead to illnesses. Sportsmen and women require a healthy diet. Current platforms such as Fuelary, Fitness Pal, Nutritics, First Beat Sports, Metfit, Precision Nutrition, Athlete Analyzer, and Nutrient Pro assist athletes in tracking their objectives and activity levels. The development of a nutrition advice system for athletes’ health and wellbeing is the aim of this system. It is impossible for anyone to follow their daily lifespan without eating a healthy diet. Sportspeople in particular who lack energy and are not physically strong are unable to practice or train. It gives them the knowledge they need to select the best meal for their body based on their dietary needs. In the suggested approach, machine learning techniques are used to list their diet based on their requirements. A person can choose the food groups they eat from, and meal recommendations will be displayed along with sports practice hours and nutritional needs. Two algorithms, the Random Forest method and the K Means algorithm, are used in this system to generate recommendations, and they yield accuracy rates of 86% and 78%, respectively. Compared to K Means, Random Forest forecasts food wisely.