Skip to main content

LUP Student Papers

LUND UNIVERSITY LIBRARIES

Comparing Machine Learning Models for Ukrainian Households Clustering through the Prism of Expenditures

Zadorozhnia, Lina LU (2025) DABN01 20251
Department of Economics
Department of Statistics
Abstract
Household expenditure patterns in Ukraine were explored with a focus on clustering techniques to uncover socioeconomic insights and inform policy interventions. Using an officially published Household Income Survey dataset for 2021, the research applies advanced machine learning models, including k-means, DBSCAN, and PCA, to analyze spending habits and identify household profiles with distinct expenditure priorities. The findings highlight significant variations in spending, such as elevated expenditures on food, housing, and transport, offering opportunities for targeted policies to redirect spending toward savings. The comparative analysis of clustering algorithms emphasizes their strengths and limitations, with k-means demonstrating... (More)
Household expenditure patterns in Ukraine were explored with a focus on clustering techniques to uncover socioeconomic insights and inform policy interventions. Using an officially published Household Income Survey dataset for 2021, the research applies advanced machine learning models, including k-means, DBSCAN, and PCA, to analyze spending habits and identify household profiles with distinct expenditure priorities. The findings highlight significant variations in spending, such as elevated expenditures on food, housing, and transport, offering opportunities for targeted policies to redirect spending toward savings. The comparative analysis of clustering algorithms emphasizes their strengths and limitations, with k-means demonstrating simplicity and speed, while DBSCAN offers greater flexibility in handling complex data structures. (Less)
Please use this url to cite or link to this publication:
author
Zadorozhnia, Lina LU
supervisor
organization
course
DABN01 20251
year
type
H1 - Master's Degree (One Year)
subject
keywords
household expenditures, clustering, k-means, DBSCAN, PCA, Ukraine.
language
English
id
9192756
date added to LUP
2025-09-12 09:05:32
date last changed
2025-09-12 09:05:32
@misc{9192756,
  abstract     = {{Household expenditure patterns in Ukraine were explored with a focus on clustering techniques to uncover socioeconomic insights and inform policy interventions. Using an officially published Household Income Survey dataset for 2021, the research applies advanced machine learning models, including k-means, DBSCAN, and PCA, to analyze spending habits and identify household profiles with distinct expenditure priorities. The findings highlight significant variations in spending, such as elevated expenditures on food, housing, and transport, offering opportunities for targeted policies to redirect spending toward savings. The comparative analysis of clustering algorithms emphasizes their strengths and limitations, with k-means demonstrating simplicity and speed, while DBSCAN offers greater flexibility in handling complex data structures.}},
  author       = {{Zadorozhnia, Lina}},
  language     = {{eng}},
  note         = {{Student Paper}},
  title        = {{Comparing Machine Learning Models for Ukrainian Households Clustering through the Prism of Expenditures}},
  year         = {{2025}},
}