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.

Bibliographische Detailangaben
Hauptverfasser:	Boateng, Albert, Rahim, Maheen Mehjabeen
Weitere Verfasser:	Islam, Mohammad Rafiqul
Format:	Abschlussarbeit
Sprache:	English
Veröffentlicht:	Brac University 2024
Schlagworte:	Statistical analysis Routing matrix Adjacency matrix Machine learning Regression models Traffic matrix Network data flows Network analysis (Planning). Regression analysis.
Online Zugang:	http://hdl.handle.net/10361/24226

id	10361-24226
record_format	dspace
spelling	10361-242262024-09-30T21:01:16Z Statistical analysis of network data flows and predictions using statistical and machine learning regression models Boateng, Albert Rahim, Maheen Mehjabeen Islam, Mohammad Rafiqul Department of Mathematics and Natural Sciences, BRAC University Statistical analysis Routing matrix Adjacency matrix Machine learning Regression models Traffic matrix Network data flows Network analysis (Planning). Regression analysis. This thesis is submitted in partial fulfilment of the requirements for the degree of Bachelor of Science in Mathematics, 2024. Catalogued from the PDF version of the thesis. Includes bibliographical references (pages 47-48). 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.Sc. in Mathematics 2024-09-30T04:32:06Z 2024-09-30T04:32:06Z ©2024 2024-05 Thesis ID 20216003 ID 23216010 http://hdl.handle.net/10361/24226 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. 57 pages application/pdf Brac University
institution	Brac University
collection	Institutional Repository
language	English
topic	Statistical analysis Routing matrix Adjacency matrix Machine learning Regression models Traffic matrix Network data flows Network analysis (Planning). Regression analysis.
spellingShingle	Statistical analysis Routing matrix Adjacency matrix Machine learning Regression models Traffic matrix Network data flows Network analysis (Planning). Regression analysis. 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	Islam, Mohammad Rafiqul
author_facet	Islam, Mohammad Rafiqul 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/10361/24226
work_keys_str_mv	AT boatengalbert statisticalanalysisofnetworkdataflowsandpredictionsusingstatisticalandmachinelearningregressionmodels AT rahimmaheenmehjabeen statisticalanalysisofnetworkdataflowsandpredictionsusingstatisticalandmachinelearningregressionmodels
_version_	1814307448372068352

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

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