Clustering Analysis of Statewide Health Insurance Marketplaces
Key words: R Studio, Stata, Unsupervised Learning
• Conducted data cleaning and exploration on a 2019 plan-level health insurance dataset from the United States, ensuring that the data was accurate and ready for analysis.
• Explored the dataset to analyze geographic variations in health insurance marketplaces and identify patterns and trends using statistical methods.
• Conducted state-level clustering analysis and used ggplot to identify ideological variations in insurance plans, highlighting the importance of customized visualization tools in data analysis.
• Found large differences in plan policies in the marketplaces between states, aligning with the initial hypothesis, and used data-driven insights to better understand the factors driving these differences.
• Attempted to predict state health insurance coverage rate on a contemporary level, and results showed that while large variations exist in health insurance structure, they do not have a significant effect on evidence-based care, underscoring the importance of data-driven decision making in healthcare policy.
Oct 2022 - Dec 2022
Consumer Behavior Analysisi
Key words: R Studio, Machine Learning, A/B Testing
• Conducted market analysis and compared sustainable marketing strategies of L’Oreal and Shiseido through extensive research, customer interviews, and feature request analysis.
• Utilized advanced staKsKcal models including exponential smoothing, ARIMA, and principal component analysis to analyze consumer trends and predict future purchase rates.
• Designed and executed A/B testng to evaluate advertising strategies and optimize critical metrics for increased customer engagement and conversion rates.
Feb 2019 - Jun 2019