Weon Gyu

Weon Gyu Jeon

Junior BI Analyst | Data cleaning, analysis & visualization

New to Contra

Weon Gyu is ready for their next project!

Cover image for Who really took advantage on US-China trade war? Data from J...
Who really took advantage on US-China trade war? Data from June 2024 to May 2025 suggests: - CPI, PPI, Employment data has not shifted dramatically for both countries, after levying tariff to each other. - China had enormous amount of trade surplus against US; it slightly declined after Trump's inaguration. - GDP Growth declined for both countries after Q1 2025, US GDP swinging lot more to the ground.
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Cover image for 😊 True Factors of Happiness (Tableau • Data Analysis) A int...
😊 True Factors of Happiness (Tableau • Data Analysis) A interactive project exploring what truly drives happiness, contrasting common beliefs with scientifically supported findings. Key work: - Integrated data from GDP, ESS11 survey, and personality datasets (extraversion, social behaviors, trust). - Built dashboards showing why perceived status and social behavior predict happiness more reliably than income. - Constructed a clear narrative using evolutionary psychology principles. - Tools/Languages: Tableau, Excel, SQL, SSMS
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Cover image for 📊 AdventureWorks KPI Dashboard (SQL Server • Tableau BI Das...
📊 AdventureWorks KPI Dashboard (SQL Server • Tableau BI Dashboard) A professional KPI dashboard built on the AdventureWorks database, designed to mimic real-world BI workflows. Key work: - Wrote all SQL extract queries for: Revenue, Profit, Orders, KPIs, Inventory, HR metrics. - Built four dashboard pages: Executive Overview (crucial KPIs), Product (categorical performance), Customer (retention and segmentation), HR (employee performance) - Languages/Tools: Tableau, Excel, SQL, SSMS.
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Cover image for 🏀 NBA Salary Prediction (Python • ML/DL) A full end-to-end ...
🏀 NBA Salary Prediction (Python • ML/DL) A full end-to-end data science project predicting next-season NBA player salaries using machine learning and deep learning. Key work: - Collected and cleaned 2010–2025 NBA traditional stats and salary data. - Built linear regression, random forest, and deep learning models for comparison. - Solved multicollinearity issues, engineered features, and optimized VIF. - Languages/Libraries: Python, Pandas, NumPy, Scikit-learn, PyTorch.
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