Department of Mathematics
Permanent URI for this communityhttps://repository.cuilahore.edu.pk/handle/123456789/21
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Item House Price Prediction by using Machine Learning(Library Information Services, COMSATS University Islamabad, Lahore Campus, 2024) Laiba Faisal (CIIT/FA20-BSM-057/LHR), Ameema Umar (CIIT/FA20-BSM-053/LHR); Dr. Hani Shaker; LHR TP 9909The goal of this project is to create a machine learning model that can predict home selling prices by taking into account variables like location, square footage, number of bedrooms and bathrooms, and other relevant characteristics. The model prioritizes optimization for managing missing data in order to produce trustworthy estimates for the real estate market. This study advances real estate predictive modelling by using rigorous analysis and experimentation. It provides practitioners and stakeholders with useful information for making precise cost projections.Item Network Analysis Using Graph Indices(Library Information Services, COMSATS University Islamabad, Lahore Campus, 2024) Shamsa Liaqat (CIIT/FA-BSM-010/LHR), Mahnoor (CIIT/FA20-BSM-043/LHR); Dr. Hani Shaker; LHR TP 9917In the digital age, social networking platforms like Twitter, LinkedIn, and Facebook have significantly influenced how individuals interact and form communities. These platforms, along with transportation networks, play crucial roles in shaping social dynamics and fa- cilitating physical mobility. This thesis employs graph theory to analyze these networks, focusing on the complexities of their interactions using Graph Indices, a mathematical framework that enhances the precision of network analysis. Graph theory provides a robust foundation for understanding the structure and flow of information within networks. This study specifically utilizes concepts such as betweenness centrality and the beta index to an- alyze network properties. Betweenness centrality identifies key influencers by measuring how often a node lies on paths between other nodes, while the beta index assesses network complexity by calculating the ratio of edges to vertices. The research encompasses a de- tailed examination of subgraphs, which reveal clusters or communities within the network, providing insights into user behavior and network dynamics. This analysis is applied to various social networks and transportation systems, demonstrating how graph indices can be used to optimize and enhance network structures. By integrating Graph Indices into graph theory, this study offers a more versatile framework for capturing social interactions’ complexities. The findings underscore the importance of mathematical tools in developing strategies for managing and leveraging social networks, leading to improved efficiency and robustness of network systems. This research not only advances theoretical understand- ing but also provides practical solutions for network analysis, paving the way for future applications in the field.Item Network Analysis Using Graph Indices(Library Information Services, COMSATS University Islamabad, Lahore Campus, 2024) Shamsa Liaqat (FA20-BSM-010) : Mahnoor (FA20-BSM-043); Dr. Hani Shaker; LHR TP 9917In the digital age, social networking platforms like Twitter, LinkedIn, and Facebook have significantly influenced how individuals interact and form communities. These platforms, along with transportation networks, play crucial roles in shaping social dynamics and fa cilitating physical mobility. This thesis employs graph theory to analyze these networks, focusing on the complexities of their interactions using Graph Indices, a mathematical framework that enhances the precision of network analysis. Graph theory provides a robust foundation for understanding the structure and flow of information within networks. This study specifically utilizes concepts such as betweenness centrality and the beta index to an alyze network properties. Betweenness centrality identifies key influencers by measuring how often a node lies on paths between other nodes, while the beta index assesses network complexity by calculating the ratio of edges to vertices. The research encompasses a de tailed examination of subgraphs, which reveal clusters or communities within the network, providing insights into user behavior and network dynamics. This analysis is applied to various social networks and transportation systems, demonstrating how graph indices can be used to optimize and enhance network structures. By integrating Graph Indices into graph theory, this study offers a more versatile framework for capturing social interactions’ complexities. The findings underscore the importance of mathematical tools in developing strategies for managing and leveraging social networks, leading to improved efficiency and robustness of network systems. This research not only advances theoretical understand ing but also provides practical solutions for network analysis, paving the way for future applications in the field.