[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119912-en":3,"doc-seo-119912-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},119912,3848291630094,"Emma Wilson","https://eur-avatar.wpscdn.com/davatar_085a072bc5b1113ac321206ff7593b45",8,"Research & Report","Estimating Air Pollution Levels Using Machine Learning - A Project Report","Air pollution has become a major global concern, especially due to the presence of PM2.5 particles with diameters of 2.5 micrometers or less. Because of their small size, these particles pose serious health risks by infiltrating the lungs and contributing to various health problems. With rising demand for practical air-quality awareness, the work proposes an ensemble machine learning approach to estimate air pollution efficiently, reducing reliance on multi-instrument setups and lowering equipment cost and maintenance burden.","San Jose State University  \nSJSU ScholarWorks  \n\n| Master's Projects | Master's Theses and Graduate Research |\n| --- | --- |\n| Fall 2023\u003Cbr>Estimating Air Pollution Levels Using Machine Learning Srujay Rao Devaraneni\u003Cbr>Follow this and additional works at: [https://scholarworks.sjsu.edu/etd_projects](https://scholarworks.sjsu.edu/etd_projects)\u003Cbr> Part of the Other Computer Engineering Commons |  |\n\nRecommended Citation  \nDevaraneni, Srujay Rao, \"Estimating Air Pollution Levels Using Machine Learning\" (2023) . Master 's Projects. 1334.  \n[https://scholarworks.sjsu.edu/etd_projects/1334](https://scholarworks.sjsu.edu/etd_projects/1334)  \nThis Master's Project is brought to you for free and open access by the Master's Theses and Graduate Research at SJSU ScholarWorks. It has been accepted for inclusion in Master's Projects by an authorized administrator of SJSU ScholarWorks. For more information, please contact [scholarworks@sjsu.edu](scholarworks@sjsu.edu).  \nEstimating Air Pollution Levels Using Machine Learning  \nA Project Report  \nPresented To  \nProf. Robert Chun Department of Computer Science San Jose State University  \nIn Partial Fulfilment Of the Requirements for the class Fall-2023: CS 298  \nBy  \nSrujay Rao Devaraneni  \nOctober 2023  \nThe Designated Project Committee Approves the Master’s Project Titled Estimating Air Pollution Levels Using Machine Learning  \nBy  \nSrujay Rao Devaraneni  \nAPPROVED FOR THE DEPARTMENT OF COMPUTER SCIENCE  \nSAN JOSE STATE UNIVERSITY  \nDr. Robert Chun  \nDr. William Andreopoulos  \nNikhila Saini  \nDepartment of Computer Science  \nDepartment of Computer Science Oracle Cloud Infrastructure  \nACKNOWLEDGEMENT  \nI would like to convey my profound gratitude to Dr. Robert Chun for his priceless guidance, steadfast motivation, and unwavering support throughout the duration of this project. His profound knowledge and insightful advice were instrumental in navigating challenging situations where viable solutions seemed elusive. Dr. Chun's patience and consistent provision of both moral and technical support were pivotal in sustaining my progress on this project.  \nI wish to express my sincere thanks to Dr. William Andreopoulos, and Miss. Nikhila Saini, who were part of my defense committee, as well as the entire CS faculty. Their collective support has been a cornerstone in my academic journey over the past two years, manifesting in various forms.  \nFinally, I would like to extend my appreciation to my friends, and family, whose consistent support and motivation have been the driving force behind this undertaking.  \nABSTRACT  \nAir pollution has emerged as a substantial concern, especially in developing countries worldwide. An important aspect of this issue is the presence of PM2 .5. Air pollutants with a diameter of 2.5 or less micrometers are known as PM2 .5. Due to their size, these particles are a serious health risk and can quickly infiltrate the lungs, leading to a variety of health problems. Due to growing concerns about air pollution, technology like automatic air quality measurement can offer beneficial assistance for both personal and business decisions. This research suggests an ensemble machine learning model that can efficiently replace the standard air quality estimation techniques, which need several instruments and setup and have large financial expenditures for equipment acquisition and maintenance.  \nIndex Terms-machine learning, neural network, PM 2.5, prediction model, regression model  \nDEFINITION OF TERMS  \nPM 2.5 – Particulate matter with diameter less than 2.5 micrometers  \nTABLE OF CONTENTS  \nI. Introduction ..................................................................................................... 7  \nII. Literature Review .......................................................................................... 10  \nIII. Research Methodology .................................................................................. 21  \nIV. Data Pre-Processing..................","cbCaieWK0LdrcCQf","https://ap.wps.com/l/cbCaieWK0LdrcCQf","pdf",7497505,1,54,"English","en",105,"# Introduction\n# Literature Review\n# Research Methodology\n# Data Pre-Processing\n# Base Learners\n# Architecture\n# Experimental Settings\n# Meta Learner\n# Evaluation Metrics\n# Results and Discussion\n# Conclusion\n# References","[{\"question\":\"What health-related concern does the project focus on?\",\"answer\":\"The project targets air pollution risk from PM2.5, whose particles are small enough to infiltrate deep into the lungs and contribute to health problems.\"},{\"question\":\"Why does the project propose machine learning for air quality estimation?\",\"answer\":\"It aims to replace standard estimation approaches that require several instruments, complex setup, and high costs for equipment acquisition and maintenance.\"},{\"question\":\"What modeling approach is used in the research?\",\"answer\":\"The study suggests an ensemble machine learning model, built around base learners and a meta learner, to improve air pollution prediction.\"}]","Estimating Air Pollution Levels Using Machine Learning - 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