Diet Recommendation System based on Different Machine Learners

Authors

  • Megh Shah  Research Schoar, Department of Computer Engineering, Sigma Institute of Engineering, Vadodara, Gujarat, India
  • Sheshang Degadwala  Associate Professor, Department of Computer Engineering, Sigma Institute of Engineering, Vadodara, Gujarat, India
  • Dhairya Vyas   Managing Director, Shree Drashti Infotech LLP, Vadodara, Gujarat, India

DOI:

https://doi.org//10.32628/CSEIT228249

Keywords:

Diet Recommendation, Machine Learning, Clustering, Health Factors, vegetarian and non-vegetariana

Abstract

In today's culture, many people suffer from a range of ailments and illnesses. It's not always simple to recommend a diet right away. The majority of individuals are frantically trying to reduce weight, gain weight, or keep their health in check. Time has also become a potential stumbling block. The study relies on a database that has the exact amounts of a variety of nutrients. As a result of the circumstance, we set out to create a program that would encourage individuals to eat healthier. Only three sorts of goods are recommended: weight loss, weight gain, and staying healthy. The Diet Recommendation System leverages user inputs such as medical data and the option of vegetarian or non-vegetarian meals from the two categories above to predict food items. We'll discuss about food classification, parameters, and machine learning in this post. This research includes different machine learner K-nearest neighbor, Support vector machine, Decision Tree, Navier buyers, Random Forest and Extra tree classifier comparative analysis for future diet plan prediction.

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IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology

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Published

2022-05-03

Issue

Volume 8, Issue 3, May-June-2022

Section

Research Articles

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How to Cite

[1]
Megh Shah, Sheshang Degadwala, Dhairya Vyas , " Diet Recommendation System based on Different Machine Learners, IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 8, Issue 3, pp.01-10, May-June-2022. Available at doi : https://doi.org/10.32628/CSEIT228249