Diet Recommendation System Using K-Means Clustering Algorithm of Machine Learning
Nidhi Waghela,
Jahanvi Mistry,
Melony Bharucha and
Monali Parikh
International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 2024, vol. 10, issue 6, 567-571
Abstract:
In today’s world, many people suffer from range of illnesses due to lack of nutrients in their daily diet. It’s not always simple to recommend diet right away. The majority of individuals in the today’s world are fanatically trying to reduce weight, gain weight, or keep their health in check. The study relies on a database that has various amount of nutrients. As a result of the circumstance, we set out to create a program that would help out individuals to become healthy. Only three orts of good are recommended weight loss, weight gain, and staying healthy. The diet recommendation system leverages the user input such as, name, age, height, weight which calculate BMI and provide necessary diet based on the option of vegetarian or non-vegetarian meals from three categories which are weight gain, weight loss, and staying healthy. We’ll discuss about the classification of food based on machine learning in this post. This research includes K-means Clustering algorithm for future diet plan prediction.
Keywords: Diet Recommendation; Machine Learning; Clustering; Health Factors; Vegetarian and Non-Vegetarian (search for similar items in EconPapers)
Date: 2024
Note: Article URL: https://ijsrcseit.com/home/article/view/CSEIT2410445
References: Add references at CitEc
Citations:
Downloads: (external link)
https://ijsrcseit.com/home/article/view/CSEIT2410445 Article URL (text/html)
https://ijsrcseit.com/home/article/download/CSEIT2410445/CSEIT2410445 Full text (application/pdf)
Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:jbh:ijsrcs:v10:y2024:i6:id:447
DOI: 10.32628/CSEIT2410445
Access Statistics for this article
More articles in International Journal of Scientific Research in Computer Science, Engineering and Information Technology from International Journal of Scientific Research in Computer Science, Engineering and Information Technology
Bibliographic data for series maintained by Pankaj Sharma (USA) ().