[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120464-en":3,"doc-seo-120464-105":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},120464,962075114101,"Seraphina","https://ap-avatar.wpscdn.com/avatar/e000253a75eb197efd?x-image-process=image/resize,m_fixed,w_180,h_180&k=1780044092746381165",8,"Research & Report","Machine Learning Insights - Analyzing Factors Influencing Happiness Score","This case study examines the World Happiness Report dataset to identify key factors that significantly influence life happiness scores. Happiness is treated as a vital goal for both governments and individuals and as a reliable indicator of societal development. The study applies supervised machine learning methods, specifically regression and classification models, to classify and select essential features. Results highlight GDP per capita as the foremost determinant, followed by health life expectancy, and are supported by multiple performance metrics to validate the obtained data.","Machine Learning Insights: Analyzing Factors Influencing Happiness Score  \nJosé Nascimento da Silva  \nM.Sc. in Computer Science and Business Management  \nSupervisor:  \nPhD Sancho Moura Oliveira, Associate Professor, ISCTE-IUL  \nJune 2024  \n\"Happiness can change, and does change, according to the quality of the society  \nin which people live.\"  \nJohn F. Helliwell is one of the chief editors of the World Happiness Report.  \nDireitos de cópia ou Copyright  \n©Copyright: José Nascimento da Silva.  \nO Iscte-Instituto Universitário de Lisboa tem o direito, perpétuo e sem limites geográficos, de arquivar e publicitar este trabalho através de exemplares impressos reproduzidos em papel ou de forma digital, ou por qualquer outro meio conhecido ou que venha a ser inventado, de o divulgar através de repositórios científicos e de admitir a sua cópia edistribuição com objetivos educacionais ou de investigação, não comerciais, desde que sejadado crédito ao autor e editor.  \nAgradecimentos  \nEm primeiro lugar, gostaria de expressar a minha mais sincera gratidão, apoio, confiança, dedicação e disponibilidade aos meus orientadores do Prof. Dr. Sancho Moura Oliveira.  \nDe seguida, deixo um agradecimento especial à minha família, em especial aosmeus pais, irmã e à Maria, pelo exemplo de resiliência, entrega e por serem o meu pilar.  \nPor último, agradeço aos meus companheiros desta viagem, os meus amigos. Obrigada pela motivação, pela partilha e pela companhia em todos os momentos.  \nAcknowledgements  \nFirst and foremost, I would like to express my sincerest gratitude, support, trust, dedication, and availability to my supervisors of Prof. Dr. Sancho Moura Oliveira.  \nNext, I would like to thank my family, especially my parents, sister, and Maria, for their example of resilience, dedication and for being my pillar.  \nFinally, I would like to thank to my partners in this adventure, my friends. Thank you for the motivation and support.  \nResumo  \nEste caso de estudo visa examinar o conjunto de dados do Relatório Mundial da Felicidade, focando na identificação de fatores-chave que influenciam significativamenteas pontuações da felicidade na vida. A felicidade serve como um objetivo vital tanto para governos quanto para indivíduos e atua como um indicador confiável do desenvolvimento social. Utilizando técnicas de machine learning, especificamente modelos de regressão e classificação, este estudo classifica e seleciona características essenciais. Os resultados, derivados de uma análise de dados abrangente, destacam que o PIB per capita como o principal determinante da felicidade na vida, seguido pela expectativa de vida. Os resultados do estudo são substanciados através de várias métricas de desempenho, assegurando a validade dos dados obtidos.  \nPalavras-Chave: “Machine Learning” e “Pontuação de Felicidade” e “Visualização de Dados”  \nAbstract  \nThis case study aims to examine the World Happiness Report dataset, focusing on identifying key factors that significantly influence life happiness scores. It posits that happiness serves as a vital goal for both governments and individuals and acts as a reliable indicator of societal development. Utilizing supervised machine learning techniques, specifically regression and classifications models, this study classifies and selects essential features. The findings, derived from comprehensive data analysis, highlight GDP per capita as the foremost determinant of life happiness, followed by health life expectancy. The study's outcomes are substantiated through various performance metrics, ensuring the validity of the obtained data.  \nkeywords: “Machine Learning” AND “Happiness Score” AND “Data Visualization”  \nGeneral Index  \nAgradecimentos ................................................................................................................ i  \nAcknowledgements ........................................................................................................... i  \nResumo .......................","cbCaikvbhHlhiBkx","https://ap.wps.com/l/cbCaikvbhHlhiBkx","pdf",3539107,1,64,"English","en",105,"# Chapter 1 – Introduction\n## Background\n## Research Purpose\n## Research Questions\n## Limitations\n## Document Outline","[{\"question\":\"What dataset does the case study analyze?\",\"answer\":\"The case study analyzes the World Happiness Report dataset to study how different factors relate to life happiness scores.\"},{\"question\":\"Which machine learning methods are used in the study?\",\"answer\":\"The study uses supervised machine learning, specifically regression and classification models, to classify and select essential features.\"},{\"question\":\"What factors are identified as the strongest determinants of happiness?\",\"answer\":\"The findings indicate that GDP per capita is the foremost determinant of life happiness, followed by health life expectancy.\"}]","Machine Learning Insights - 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