Project

Author

Affiliation

Yuxuan Chen

Boston University

Business Running Case: Evaluating Personal Job Market Prospects in 2024

Topic: Salary & Compensation Trends

• This study explores the differences in compensation between AI and non-AI occupations, the geographic distribution of high-paying AI and traditional roles, the salary comparison between remote and on-site jobs, the fastest-growing industries in terms of wages in 2024, and the key beneficiaries of the rise of AI. Using data from Lightcast and external labor market sources, it focuses on pay disparities across disciplines and examines how job seekers can effectively position themselves amid evolving hiring trends, compensation patterns, AI adoption, remote work, and gender-based employment dynamics.
• Using Exploratory Data Analysis (EDA), this study examines salary comparisons between remote and on-site jobs, regional salary distributions, the highest-paying occupations, and compensation differences between AI and non-AI roles. It also compares team skills with industry demands to conduct a Skill Gap Analysis, identifying areas where the team’s capabilities may not align with market expectations.
• Machine Learning (ML): Perform cluster analysis on the dataset using K-Means to explore how different job roles group into clusters based on salary, employment type, job duration, education level, remote options, state, and other categorical factors. Additionally, build a multiple linear regression model to predict salary.
• Natural Language Processing (NLP) Approach: Apply K-Means clustering to the TF-IDF features of job descriptions to divide them into four distinct clusters, and generate word clouds for each cluster for analysis. In addition, train two different classifiers—Naive Bayes and Support Vector Machine (SVM)—to predict the corresponding job cluster based on the TF-IDF features extracted from the job descriptions.

Repository: GitHub                         Website: Visit Website

Lobster Land: Cruise Industry Expansion Consulting

Worked in a consulting team hired by “Lobster Land” to evaluate potential expansion into the cruise industry. Leveraged analytics and business insights to provide strategic recommendations across multiple domains:

• Market Analysis & Segmentation: Clustered Caribbean cruise-port data to define segments and recommend tailored itineraries and marketing.
• Conjoint Analysis: Used ratings-based conjoint modeling to identify the most appealing and cost-effective onboard experience bundles for overnight voyages; selected combinations within budget constraints.
• Predictive Modeling: Built a classification model to predict customer cancellation risk; translated model insights into actionable retention strategies.
• Forecasting & A/B Testing: Forecasted 2025 EPS for major cruise lines (Carnival & NCLH); performed A/B testing on cruise-themed marketing visuals to guide creative selection.
• Data Visualization: Created an interactive Tableau dashboard highlighting key insights from cruise industry datasets.
• Strategic Case Analysis: Developed a customer segmentation-based cruise product proposal inspired by Royal Caribbean’s Icon of the Seas.

Repository: GitHub

Domestic(U.S.) and International Equity Portfolio research

Domestic Equity Portfolio Project

• Constructed a diversified 3-stock U.S. equity portfolio based on historical return and sectoral diversification from 2000 to 2025, using S&P500 and Dow30 as benchmarks.
• Performed time-series and cross-sectional analysis of monthly holding period yields (HPYs), including mean returns, volatility, Sharpe ratio, and systematic risk (Beta).
• Estimated expected returns using both CAPM and Dividend Discount Model (DDM) for dividend-paying stocks.
• Visualized portfolio growth and comparative performance using Excel-based index plots, efficient frontiers, and return-risk plots.

International Equity Portfolio Project

• Designed a 6-country international ETF portfolio (including U.S., China, and four additional markets), incorporating both developed and emerging economies to ensure geographical and structural diversification.
• Computed returns in USD as the Reporting Currency, accounting for foreign exchange rates and using aligned monthly data from 2000 to 2025.
• Conducted return analysis, risk assessment, and correlation studies across markets to evaluate diversification benefits.
• Created and interpreted capital base index (CBI) plots, correlation matrices, and international portfolio efficient frontiers.

Repository: AD717-Domestic_n_International_Equity_Portfolio_Project