[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123441-en":3,"doc-seo-123441-105":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},123441,1374391974564,"Clementine","https://ap-avatar.wpscdn.com/avatar/14000253aa45c000a9e?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779874745381141002",8,"Research & Report","A Deep Dive into the Factors Influencing Financial Success - A Machine Learning Approach","This study examines how socioeconomic factors shape individual financial success using machine learning. Financial success is defined as an individual’s monetary gains within a one-year period, and income analysis targets systematic differences between higher- and lower-income earners. Using NLSY97 longitudinal survey data covering 8,984 individuals, the paper evaluates income variables alongside extensive socioeconomic predictors. Results show education, occupation, and gender as the top determinants, with working hours, age, and tenure as secondary influences, and other family and background variables as tertiary factors.","A Deep Dive into the Factors Influencing Financial Success: A Machine Learning  \nApproach  \nMichael Zhou1, Ramin Ramezani2  \n1Interlake High School, Bellevue, WA, [michaelzhou2025@gmail.com](michaelzhou2025@gmail.com)[ ](michaelzhou2025@gmail.com)2University of California, Los Angeles, California, [raminr@ucla.edu](raminr@ucla.edu)  \nAbstract  \nThis paper explores various socioeconomic factors that contribute to individual financial success using machine learning algorithms and approaches.  \nFinancial success, a critical aspect of all individual’s well-being, is a complex concept influenced by various factors. This study aims to understand the determinants of financial success. It examines the survey data from the National Longitudinal Survey of Youth 1997 by the Bureau of Labor Statistics (1), consisting of a sample of 8,984 individuals’s longitudinal data over years. The dataset comprises income variables and a large set of socioeconomic variables of individuals.  \nAn in-depth analysis shows the effectiveness of machine learning algorithms in financial success research, highlights the potential of leveraging longitudinal data to enhance prediction accuracy, and provides valuable insights into how various socioeconomic factors influence financial success.  \nThe findings highlight the significant influence of highest education degree, occupation and gender as the top three determinants of individual income among socioeconomic factors examined. Yearly working hours, age and work tenure follow as three secondary influencing factors, and all other factors including parental household income, industry, parents’ highest grade and others are identified as tertiary factors.  \nThese insights allow researchers to better understand the complex nature of financial success, and are also crucial for fostering financial success among individuals and advancing broader societal well-being by providing insights for policymakers during decision-making process.  \nKeywords  \nMachine learning, financial success, socioeconomic factors, longitudinal data, individual income, NLSY1997  \nIntroduction  \nIn the contemporary world, financial success and achieving financial success has gradually gained more and more weight in the minds of all individuals. As a result of the capitalist economy under which much of the world now operates on, the notion of financial success, which once was a personal ambition, has become a worldly phenomenon with many far-reaching implications. Recognizing and navigating the intricacies of financial success is one of the most paramount endeavors that researchers must take in order to grasp the force that shapes aspirations, decision-making, and the broader socio-economic fabric of society.  \nTo better understand the complexities associated with financial success and income analysis, we will start off by defining some terms. “Financial success” in the context of this study encompasses the monetary gains of individuals by the same individuals in a one year time-frame.  \n“Income analysis” refers to the systematic evaluation of patterns between those who earn a higher income and those who earn a lower income. The research of these key concepts will help dissect the complex nature of financial success and provide nuanced insights into the factors shaping income dynamics.  \nObjectives  \nIn this big data era, advanced data science has been increasingly used in many industries, including economic research. There is a growing interest in utilizing machine learning methods in economic research, surpassing traditional statistical models. Papers (2-5) in Literature Review are some examples using machine learning methods in economic research.  \nOne prominent effort in this field of study is to predict individual income based on a range of socioeconomic factors. These factors typically include education, employment, demographic information, socioeconomic status, and other relevant variables. Papers (6-13) in Literature Review provide illust","cbCaiuLSHRXwMQrf","https://ap.wps.com/l/cbCaiuLSHRXwMQrf","pdf",1908476,1,22,"English","en",105,"# Introduction\n# Objectives\n# Literature Review\n# Methodology\n# Results and Findings\n# Implications for Policy and Well-being","[{\"question\":\"How does the study define financial success and income analysis?\",\"answer\":\"Financial success is defined as individuals’ monetary gains within a one-year timeframe. Income analysis refers to evaluating patterns that distinguish higher-income earners from lower-income earners.\"},{\"question\":\"What dataset and prediction approach does the paper use?\",\"answer\":\"The study uses NLSY97 survey data from the National Longitudinal Survey of Youth 1997, applying machine learning to perform a multi-class classification task for predicting individual income levels.\"},{\"question\":\"Which factors most strongly influence individual income in the findings?\",\"answer\":\"The top three determinants are the highest education degree, occupation, and gender. Secondary factors include yearly working hours, age, and work tenure, while remaining variables such as parental household income and parents’ highest grade act as tertiary factors.\"}]","A Deep Dive into the Factors Influencing Financial Success - A Machine Learning Approach | PDF",1785816523,55,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"a-deep-dive-into-the-factors-influencing-financial-success-a-machine-learning-approach","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/a-deep-dive-into-the-factors-influencing-financial-success-a-machine-learning-approach/123441/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-04",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"How does the study define financial success and income analysis?","Question",{"text":75,"@type":76},"Financial success is defined as individuals’ monetary gains within a one-year timeframe. Income analysis refers to evaluating patterns that distinguish higher-income earners from lower-income earners.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What dataset and prediction approach does the paper use?",{"text":80,"@type":76},"The study uses NLSY97 survey data from the National Longitudinal Survey of Youth 1997, applying machine learning to perform a multi-class classification task for predicting individual income levels.",{"name":82,"@type":73,"acceptedAnswer":83},"Which factors most strongly influence individual income in the findings?",{"text":84,"@type":76},"The top three determinants are the highest education degree, occupation, and gender. Secondary factors include yearly working hours, age, and work tenure, while remaining variables such as parental household income and parents’ highest grade act as tertiary factors.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]