Machine Learning in Heterogeneous Data Structures for Propagation

A tétel rövid nézete
dc.contributor.advisorUjvári, Balázs
dc.contributor.authorNkadimeng, Marothi Amber
dc.contributor.departmentDE--Informatikai Kar
dc.date.accessioned2025-02-22T23:42:09Z
dc.date.available2025-02-22T23:42:09Z
dc.date.created2024-11-19
dc.description.abstractThis thesis reflects my deep commitment to addressing the pressing issue of air pollution through innovative technological solutions. By integrating diverse data sources like air quality sensors, weather patterns, and traffic information, I explored how machine learning can be used to predict and manage pollution levels more effectively. Developing this project allowed me to combine my passion for data science with real-world environmental challenges, offering insights into how urban planning and policy can be improved for cleaner air. The forecasting model I created aims to protect vulnerable populations, reduce health risks, and support sustainable urban development. This work is more than a research project; it is my small but meaningful step toward a future with healthier, more sustainable cities. Through this thesis, I hope to inspire actionable change and contribute to preserving our environment for generations to come.
dc.description.courseProgramtervező informatikus
dc.description.degreeBSc/BA
dc.format.extent59
dc.identifier.urihttps://hdl.handle.net/2437/387507
dc.language.isoen
dc.rights.accessHozzáférhető a 2022 decemberi felsőoktatási törvénymódosítás értelmében.
dc.subjectmachine learning
dc.subjectdata science
dc.subjectpython
dc.subject.dspaceInformatics
dc.titleMachine Learning in Heterogeneous Data Structures for Propagation
dc.title.subtitleBig Data solutions for the forecast of the air pollution
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