This paper explores various socioeconomic factors that contribute to
individual financial success using machine learning algorithms and approaches.
Financial success, a critical aspect of all individual's well-being, is a
complex concept influenced by a plethora of different factors. This study aims
to understand the true 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.
An in-depth analysis demonstrates 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.
The findings underscore 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 emerge as three secondary influencing factors, and all other factors
including parental household income, industry, parents' highest grade and
others are identified as tertiary factors.
These insights allow researchers to better understand the complex nature of
financial success and enable policymakers to grasp the underlying dynamics
shaping aspirations, decision-making, and the broader socio-economic fabric of
society. This comprehension is crucial for fostering financial success among
individuals and advancing broader societal well-being.

探索机器学习算法和方法在个体金融成功中所发挥的各种社会经济因素，以理解和揭示影响金融成功的真正决定因素，其中包括最高学历、职业、性别、年工作小时、年龄和工作资历等。