Consumption of Food by College Students using Ml Algorithms

Abstract

We are well aware of the many problems that our current generations are facing. From all these new enhancements in the real world, it has been quite hard for them to keep up with everything evolving around them. Keeping all this in mind, they work day in and out to make sure that their knowledge of their surroundings up to date. However, we believe that they fail to take care of themselves in the process correctly. No matter how much a particular individual may withstand in terms of workload, stress, or other mental & emotional barriers, our physical body will always be the critical aspect to overcoming them. Most people believe that working out and maintaining physical fitness are the significant aspects to sustain an excellent physical form, but they simply overlook the most crucial element which is their eating habits [1]. Although our body may be physically fit, the nourishment of our body depends on the eating styles that we follow on a day to day basis. Food is what nourishes our body with most of the proteins & minerals that we require; without it, we wouldn't be able to accomplish much. On conducting worldwide research on people's lifestyles, we were able to conclude that over the past 33 years, the obesity rate among human beings has increased by a mere 27.5%. What seems to be the most thoughtful yet intriguing fact is that although many people are overweight as well as obese, they still believe that their eating habits are healthy [2]. Most people are living in the dilemma of the fact that they maintain a healthy lifestyle. We aim to study the views on a healthy lifestyle as per the norms of our current generation. We want to analyze their daily eating habits as well as their thoughts on their lifestyle. So the question that remains is “What exactly is a Healthy Eating Lifestyle?”[3]To know the accuracy of our data set we use the machine learning algorithms like Support Vector Machine (svm), Naïve-Bayes Classifier which are used for both classification and regression [4].