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Exploring the Applications of Machine Learning in the Public Sector

Hammarberg Dalmyr, Adrian LU (2023) FYTK03 20231
Department of Physics
Computational Biology and Biological Physics - Has been reorganised
Abstract
Despite the many use cases for machine learning, it sees minimal usage in Sweden’s public sector today. It is important that the public sector in particular utilizes the most efficient tools available. For this reason, the Department of Employment and Social Services in Malmö has started a project to explore the viability of introducing machine learning to their work. This thesis corresponds to roughly the first half of the project. In the first stages, data pre-processing was required to get the data in a machine readable format. The data was then visualized using two different methods. Unsupervised learning was applied through the use of hierarchical clustering. Both visualization and clustering are heavily dominated by binary variables.... (More)
Despite the many use cases for machine learning, it sees minimal usage in Sweden’s public sector today. It is important that the public sector in particular utilizes the most efficient tools available. For this reason, the Department of Employment and Social Services in Malmö has started a project to explore the viability of introducing machine learning to their work. This thesis corresponds to roughly the first half of the project. In the first stages, data pre-processing was required to get the data in a machine readable format. The data was then visualized using two different methods. Unsupervised learning was applied through the use of hierarchical clustering. Both visualization and clustering are heavily dominated by binary variables. An autoencoder was used in order to reduce noise and dimensionality, but the dependence on certain binary features remained. The last step is supervised learning in the form of a random forest classification model, with the goal to predict individuals’ time spent at and financial aid received from the Department. This model appears able to extract information beyond the binary variables. (Less)
Please use this url to cite or link to this publication:
author
Hammarberg Dalmyr, Adrian LU
supervisor
organization
course
FYTK03 20231
year
type
M2 - Bachelor Degree
subject
language
English
id
9134114
date added to LUP
2023-08-21 09:30:20
date last changed
2023-08-30 14:52:36
@misc{9134114,
  abstract     = {{Despite the many use cases for machine learning, it sees minimal usage in Sweden’s public sector today. It is important that the public sector in particular utilizes the most efficient tools available. For this reason, the Department of Employment and Social Services in Malmö has started a project to explore the viability of introducing machine learning to their work. This thesis corresponds to roughly the first half of the project. In the first stages, data pre-processing was required to get the data in a machine readable format. The data was then visualized using two different methods. Unsupervised learning was applied through the use of hierarchical clustering. Both visualization and clustering are heavily dominated by binary variables. An autoencoder was used in order to reduce noise and dimensionality, but the dependence on certain binary features remained. The last step is supervised learning in the form of a random forest classification model, with the goal to predict individuals’ time spent at and financial aid received from the Department. This model appears able to extract information beyond the binary variables.}},
  author       = {{Hammarberg Dalmyr, Adrian}},
  language     = {{eng}},
  note         = {{Student Paper}},
  title        = {{Exploring the Applications of Machine Learning in the Public Sector}},
  year         = {{2023}},
}