Child Poverty Measurement: A machine learning approach
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Autor:innen
Autor:in (Körperschaft)
Publikationsdatum
2020
Typ der Arbeit
Master
Studiengang
Sammlung
Typ
11 - Studentische Arbeit
Herausgeber:innen
Herausgeber:in (Körperschaft)
Betreuer:in
Übergeordnetes Werk
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Verlag / Herausgebende Institution
Hochschule für Wirtschaft FHNW
Verlagsort / Veranstaltungsort
Olten
Auflage
Version
Programmiersprache
Abtretungsempfänger:in
Praxispartner:in/Auftraggeber:in
Zusammenfassung
Child poverty is still a severe issue. One of the UN Sustainable Development Goals (SDG) is to end child poverty by 2030. In order to achieve this goal, it is crucial to target poor children and help them accurately. Nevertheless, child poverty is complex. There is no one-size-fits-all child poverty measurement. Currently, there are three most popular methodologies of child poverty measurement. They are Multidimensional Poverty Index (MPI), Bristol Approach and Multiple Overlapping Deprivations Analysis (MODA). These methods are complicated. To implement these methods need expertise and much time. Besides, for each new dataset, the processes must be implemented again. In this thesis, a machine learning approach to child poverty measurement is developed. The training data is from DHS Guinea 2018. The models of neural network, gradient boosting, random forest, linear SVC and KNN are built and trained. After a comparison of the accuracy and ROC of the models in the test dataset and cross-validation, the models of gradient boosting of all three age groups achieved the best performance. Three best models after feature selection according to three age groups (0-4 years old, 5-14 years old, 15-17 years old) are chosen. By employing a machine learning approach, with the trained machine learning model, it can measure child poverty automatically with new dataset.
Schlagwörter
Fachgebiet (DDC)
Veranstaltung
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ISBN
ISSN
Sprache
Englisch
Während FHNW Zugehörigkeit erstellt
Ja
Zukunftsfelder FHNW
Publikationsstatus
Begutachtung
Open Access-Status
Lizenz
Zitation
Ming, N. X. (2020). Child Poverty Measurement: A machine learning approach [Hochschule für Wirtschaft FHNW]. https://irf.fhnw.ch/handle/11654/40363