I transform financial data into insights that drive business performance.
As a Revenue Data Analyst, I focus on financial planning and growth analytics. I've built cash flow forecasts, refined LTV models, and created the reporting infrastructure that gives teams a clearer picture of where the business is heading — and why. I don't just answer questions with data. I find the ones worth asking.
In my free time, I play basketball, experiment in the kitchen, and read more than I probably should. Lately pickleball has taken over — always up for a game.
A full revenue analysis workbook designing and delivering a year-over-year comparison that gave management a data-driven view of what was driving performance across five product lines, 20 months of data, and a full price-volume decomposition, covering the August YTD period across FY2024 and FY2025.
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The dashboard summarizes unicorns across industries, highlights the investors backing them, and presents their valuations, providing a clear overview of the broader unicorn ecosystem.
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The analysis will cover crucial areas such as revenue and retention trends, cohort behavior, the impact of discounts on pricing, and customer segmentation. Additionally, more advanced techniques, including lifetime value (LTV) forecasting, cross-selling analysis will be employed to provide deeper insights into customer behavior and business performance.
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In this project, I performed exploratory analysis to understand the fraud transactions on a bank simulated dataset, and I also applied statistical methods to detect fraudulent activities.
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To understand the development of the electric vehicle market, this project studies the data of electric vehicles registered in WA from 1997 to 2023.
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In this project, I performed analysis on customers' reviews on an E-commerce site to identify their areas of interest/concern.
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I developed supervised learning algorithms for customer churn prediction in this project. The labelled data in this data set is imbalanced, so I applied SMOTE for oversampling. Besides, I applied encoding, standardization technique to transform the features. Logistic Regressions, KNN, Random Forest algorithms are used for modeling. Model evaluation involves metircs like f1-score, ROC and AUC scores.
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In this project, I used machine learning techniques to build models that can detect fraud credit card transactions on a highly imbalanced dataset, in which only less than 1% transactions are considered fraud. Random downsampling method is used to handle the imbalance data.
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[yaohong010@gmail.com]
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