Supervisor

Taufique Ahmed

Programme

HDIP in Data Analytics for Business

Subject

Computer Science

Abstract

Obesity is a major global public health concern, with significant implications for individual health, healthcare systems, and wider economic outcomes. Early identification of obesity risk can support preventive strategies and encourage healthier lifestyle choices before health conditions become more severe. This project develops a machine learning framework for estimating obesity levels using data relating to individuals’ physical characteristics, eating habits, lifestyle behaviours, and demographic factors. Following the CRISP-DM framework, the project examines the structure and limitations of the dataset, including its mixture of objective measurements, survey-based variables, and synthetically generated observations. Two modelling approaches are considered, one including BMI as a predictive benchmark and another excluding BMI to assess the contribution of lifestyle, behavioural, and demographic factors alone. Several classification models are evaluated using cross-validation, hyperparameter optimisation, and test-set performance, with model outputs further examined using SHAP and LIME interpretability techniques. The project aims to establish a robust and interpretable analytical framework that can support data-driven obesity prevention and risk assessment, while recognising the limitations of the dataset and the distinction between predictive modelling and clinical diagnosis.

Date of Award

2026

Full Publication Date

2026

Access Rights

open access

Document Type

Capstone Project

Resource Type

thesis

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Data Science Commons

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