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Understanding Saving Habit of Individuals for the Varied Financial Measurement Categories using Cluster Analysis

Patel, Rachita (2019) Understanding Saving Habit of Individuals for the Varied Financial Measurement Categories using Cluster Analysis. Masters thesis, Dublin, National College of Ireland.

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Abstract

Saving being a rational behaviour, it was observed that many people cannot deal with their finances which affect their long-term financial needs. Also, the economic growth of a nation depends on the Gross Domestic Saving (GDS) which is the saving from the private, public, and household sector. Thereby, this research aims at understanding the saving behaviour or saving habit of the individuals. For this purpose, the financial well-being survey data from the Consumer Financial Protection Bureau (CFPB) is being used. The saving behaviour is observed for the six categories of financial measurement. Initially, the attributes falling into these categories are identified using Multiple Linear Regression (MLR) and Random Forest (RF). Having known, the significant factors, clustering which is a type of unsupervised machine learning is being used to discover the factors influencing saving habit of individuals. Five types of clustering algorithm- K-means, Partitioning around Medoids (PAM), Clustering Large Applications (CLARA), Hierarchical, and Fuzzy (Fanny) are evaluated using internal validation metrics to find the optimal number of clusters, thereby, interpreting those results to analyse the saving behaviour. The internal validation metrics like connectivity, the Dunn’s index, and the Silhouette index has shown significant results for all the categories. Finally, the interpreted results reveal various factors leading to a good saving habit and they are education, income; also, the unmarried individuals, who owns a house, had no bad financial experience, and the ones who are goal-oriented have a good saving habit.

Item Type: Thesis (Masters)
Subjects: Q Science > QA Mathematics > Electronic computers. Computer science
T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science

Q Science > QA Mathematics > Computer software
T Technology > T Technology (General) > Information Technology > Computer software

H Social Sciences > HG Finance > Banking
Divisions: School of Computing > Master of Science in FinTech
Depositing User: Dan English
Date Deposited: 03 Jun 2020 09:52
Last Modified: 03 Jun 2020 09:52
URI: http://norma.ncirl.ie/id/eprint/4226

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