Statistical analysis of network data flows and predictions using statistical and machine learning regression models

This thesis is submitted in partial fulfilment of the requirements for the degree of Bachelor of Science in Mathematics 2024.

Библиографические подробности
Главные авторы:	Boateng, Albert, Rahim, Maheen Mehjabeen
Другие авторы:	Mohammad Rafiqul Islam
Формат:	Диссертация
Язык:	English
Опубликовано:	Brac University 2024
Предметы:	Statistical analysis Network Graph Routing matrix Traffic Matrix Adjacency matrix Machine learning(ML) Regression models Machine learning > Statistical methods Machine learning > Mathematical models
Online-ссылка:	http://hdl.handle.net/10361/23771

id	10361-23771
record_format	dspace
spelling	10361-237712024-08-19T05:40:00Z Statistical analysis of network data flows and predictions using statistical and machine learning regression models Boateng, Albert Rahim, Maheen Mehjabeen Mohammad Rafiqul Islam Department of Mathematics and Natural Sciences, BRAC University Statistical analysis Network Graph Routing matrix Traffic Matrix Adjacency matrix Machine learning(ML) Regression models Machine learning--Statistical methods Machine learning--Mathematical models This thesis is submitted in partial fulfilment of the requirements for the degree of Bachelor of Science in Mathematics 2024. Catalogued from PDF version of thesis. Includes bibliographical references (pages 55-56). This paper presents a statistical analysis of measurements relating to network’s data flows and predictions using statistical and machine learning regression models. The study’s objective is to use statistical methods and machine learning regression models to analyze and make predictions on a spatio-temporal traffic volume dataset obtained by Dr. Liang Zhao (Emory University), from sensors along two major highways in Northern Virginia and Washington, D.C. This work aims to answer some fundamental questions related to the network such as: 1. What statistical inferences and descriptive analysis can be made on the network’s data flow? 2. How can one obtain the Routine Matrix of the Network from the Adjacency Matrix? 3. How can one employ various techniques, such as Regularization and Singular Value Decomposition (SVD), to solve the singularity or ill posed nature of the network in the Traffic Matrix Estimation?, and 4. How can one apply Machine Learning regression models, such as Support Vector Regressor (SVR) and XGBoost Regressor, to make predictions on the Network’s flow volume? Concepts in this work or paper can be practically applied on other real world networks to analyze and make predictions on the network’s data flow. Albert Boateng Maheen Mehjabeen Rahim B. Mathematics 2024-08-14T06:54:40Z 2024-08-14T06:54:40Z 2024 2024-05 Thesis ID 20216003 ID 23216010 http://hdl.handle.net/10361/23771 en BRAC University theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. 56 pages application/pdf Brac University
institution	Brac University
collection	Institutional Repository
language	English
topic	Statistical analysis Network Graph Routing matrix Traffic Matrix Adjacency matrix Machine learning(ML) Regression models Machine learning--Statistical methods Machine learning--Mathematical models
spellingShingle	Statistical analysis Network Graph Routing matrix Traffic Matrix Adjacency matrix Machine learning(ML) Regression models Machine learning--Statistical methods Machine learning--Mathematical models Boateng, Albert Rahim, Maheen Mehjabeen Statistical analysis of network data flows and predictions using statistical and machine learning regression models
description	This thesis is submitted in partial fulfilment of the requirements for the degree of Bachelor of Science in Mathematics 2024.
author2	Mohammad Rafiqul Islam
author_facet	Mohammad Rafiqul Islam Boateng, Albert Rahim, Maheen Mehjabeen
format	Thesis
author	Boateng, Albert Rahim, Maheen Mehjabeen
author_sort	Boateng, Albert
title	Statistical analysis of network data flows and predictions using statistical and machine learning regression models
title_short	Statistical analysis of network data flows and predictions using statistical and machine learning regression models
title_full	Statistical analysis of network data flows and predictions using statistical and machine learning regression models
title_fullStr	Statistical analysis of network data flows and predictions using statistical and machine learning regression models
title_full_unstemmed	Statistical analysis of network data flows and predictions using statistical and machine learning regression models
title_sort	statistical analysis of network data flows and predictions using statistical and machine learning regression models
publisher	Brac University
publishDate	2024
url	http://hdl.handle.net/10361/23771
work_keys_str_mv	AT boatengalbert statisticalanalysisofnetworkdataflowsandpredictionsusingstatisticalandmachinelearningregressionmodels AT rahimmaheenmehjabeen statisticalanalysisofnetworkdataflowsandpredictionsusingstatisticalandmachinelearningregressionmodels
_version_	1814309238698147840

Statistical analysis of network data flows and predictions using statistical and machine learning regression models

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