Cover Image

Classification and prediction of obesity levels among subjects in Colombia, Peru, and Mexico using unsupervised and supervised learning

This research investigates the multifaceted relationship between various factors and obesity rates in Mexico, Peru, and Colombia using a publicly available dataset. Through Python, the study employs classification and clustering analyses, focusing on logistic regression to predict obesity levels a...

Full description

Saved in:
Bibliographic Details
Main Authors: Suhaila, Bahrom, Anuar, Ab Rani, Aisyah Amalina, Mohd Noor
Format: Article
Language:English
Published: Zenodo 2024
Subjects:
QA Mathematics
QA76 Computer software
Online Access:http://umpir.ump.edu.my/id/eprint/42159/1/Classification%20and%20Prediction%20of%20Obesity%20Levels%20among%20Subjects%20in%20Colombia%2C%20Peru%2C%20and%20Mexico%20Using%20Unsupervised%20and%20Supervised%20Learning.pdf
http://umpir.ump.edu.my/id/eprint/42159/
https://doi.org/10.5281/zenodo.12791087
Tags: Add Tag
No Tags, Be the first to tag this record!
id my.ump.umpir.42159
record_format eprints
spelling my.ump.umpir.421592024-08-05T03:45:52Z http://umpir.ump.edu.my/id/eprint/42159/ Classification and prediction of obesity levels among subjects in Colombia, Peru, and Mexico using unsupervised and supervised learning Suhaila, Bahrom Anuar, Ab Rani Aisyah Amalina, Mohd Noor QA Mathematics QA76 Computer software This research investigates the multifaceted relationship between various factors and obesity rates in Mexico, Peru, and Colombia using a publicly available dataset. Through Python, the study employs classification and clustering analyses, focusing on logistic regression to predict obesity levels and generate actionable recommendations. Combining exploratory data analysis (EDA) and advanced machine learning techniques, the research aims to unveil nuanced insights into obesity determinants. Unsupervised learning methods segmentize individuals, providing deeper insights into obesity profiles. Supervised learning algorithms like logistic regression, random forest, and adaboost classifier predict obesity levels based on labelled datasets, with random forest exhibiting superior performance. The study enhances understanding of obesity classification through machine learning and integrates data inspection, formatting, and exploration using Excel, Python, and graphical user interfaces (GUIs) such as SweetViz and PandaGui. Overall, it offers a comprehensive approach to understanding and addressing obesity using sophisticated analytical tools and methodologies. Zenodo 2024 Article PeerReviewed pdf en cc_by_4 http://umpir.ump.edu.my/id/eprint/42159/1/Classification%20and%20Prediction%20of%20Obesity%20Levels%20among%20Subjects%20in%20Colombia%2C%20Peru%2C%20and%20Mexico%20Using%20Unsupervised%20and%20Supervised%20Learning.pdf Suhaila, Bahrom and Anuar, Ab Rani and Aisyah Amalina, Mohd Noor (2024) Classification and prediction of obesity levels among subjects in Colombia, Peru, and Mexico using unsupervised and supervised learning. APS Proceedings, 13. 29-36.. (Published) https://doi.org/10.5281/zenodo.12791087 10.5281/zenodo.12791087
institution Universiti Malaysia Pahang Al-Sultan Abdullah
building UMPSA Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Pahang Al-Sultan Abdullah
content_source UMPSA Institutional Repository
url_provider http://umpir.ump.edu.my/
language English
topic QA Mathematics
QA76 Computer software
spellingShingle QA Mathematics
QA76 Computer software
Suhaila, Bahrom
Anuar, Ab Rani
Aisyah Amalina, Mohd Noor
Classification and prediction of obesity levels among subjects in Colombia, Peru, and Mexico using unsupervised and supervised learning
description This research investigates the multifaceted relationship between various factors and obesity rates in Mexico, Peru, and Colombia using a publicly available dataset. Through Python, the study employs classification and clustering analyses, focusing on logistic regression to predict obesity levels and generate actionable recommendations. Combining exploratory data analysis (EDA) and advanced machine learning techniques, the research aims to unveil nuanced insights into obesity determinants. Unsupervised learning methods segmentize individuals, providing deeper insights into obesity profiles. Supervised learning algorithms like logistic regression, random forest, and adaboost classifier predict obesity levels based on labelled datasets, with random forest exhibiting superior performance. The study enhances understanding of obesity classification through machine learning and integrates data inspection, formatting, and exploration using Excel, Python, and graphical user interfaces (GUIs) such as SweetViz and PandaGui. Overall, it offers a comprehensive approach to understanding and addressing obesity using sophisticated analytical tools and methodologies.
format Article
author Suhaila, Bahrom
Anuar, Ab Rani
Aisyah Amalina, Mohd Noor
author_facet Suhaila, Bahrom
Anuar, Ab Rani
Aisyah Amalina, Mohd Noor
author_sort Suhaila, Bahrom
title Classification and prediction of obesity levels among subjects in Colombia, Peru, and Mexico using unsupervised and supervised learning
title_short Classification and prediction of obesity levels among subjects in Colombia, Peru, and Mexico using unsupervised and supervised learning
title_full Classification and prediction of obesity levels among subjects in Colombia, Peru, and Mexico using unsupervised and supervised learning
title_fullStr Classification and prediction of obesity levels among subjects in Colombia, Peru, and Mexico using unsupervised and supervised learning
title_full_unstemmed Classification and prediction of obesity levels among subjects in Colombia, Peru, and Mexico using unsupervised and supervised learning
title_sort classification and prediction of obesity levels among subjects in colombia, peru, and mexico using unsupervised and supervised learning
publisher Zenodo
publishDate 2024
url http://umpir.ump.edu.my/id/eprint/42159/1/Classification%20and%20Prediction%20of%20Obesity%20Levels%20among%20Subjects%20in%20Colombia%2C%20Peru%2C%20and%20Mexico%20Using%20Unsupervised%20and%20Supervised%20Learning.pdf
http://umpir.ump.edu.my/id/eprint/42159/
https://doi.org/10.5281/zenodo.12791087
_version_ 1822924567543283712
score 13.235796

Similar Items