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Cleaned a messy e-commerce order dataset — fixed inconsistent text formatting, removed duplicates, and handled missing values. Applied the IQR (Interquartile Range) method to statistically detect price outliers, flagging 2 anomalous orders out of 14 for review. Calculated Q1, Q3, and outlier bounds directly in Excel to support accurate, defensible data quality decisions.
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When does a sports dashboard need the court to be more than a background?
In this case, we were designing Deuce a tennis analytics platform that needed to make complex match data feel immediate and spatial, not just numerical.
The brief was clear: analysts and coaches don't just want stats. They want to feel like they're watching the match while reading the data.
So we made the Wimbledon court the literal center of the interface. Every panel orbits it match overview and surface performance on the left, live scoreboard and tournament state on the right, performance trend running the full width below. The court isn't decoration. It's the anchor that makes every number feel like it belongs to a real moment on a real surface.
72.34% win rate. 48 aces. 210 km/h best serve. Djokovic leading Alcaraz 6-4 on Center Court. Alcaraz vs Sinner tonight at 11 PM. Tactic heatmaps showing where points are won and lost.
What we noticed was that spatial context changed how the data felt. The same numbers in a standard table read as analysis. Wrapped around a court, they read as intelligence.
Do you think sports UI design is still underestimating how much context and spatial layout affect how data is understood?
Nice aesthetics, but composition feels bottom-heavy.
This portfolio sample shows how I evaluate AI agents and automated workflows to find reliability problems, broken logic, and weak edge-case handling. I review whether the system completes the intended task, uses tools correctly, follows instructions, handles ambiguity, and produces an output that can actually be trusted.
The work focuses on identifying failure points before they become real user problems, including incomplete task execution, incorrect assumptions, workflow dead ends, hallucinations, and verification gaps. This is a portfolio demonstration of my AI agent evaluation and red-teaming work. No confidential client information is included.
Thorough breakdown! Robust edge-case handling and reliability checks are definitely the trickiest part of building reliable AI agents 🤖📊
I designed this specialized dashboard to analyze how global geopolitical conflicts and wars directly impact crude oil prices, stock markets, and GDP growth. The goal was to take a highly complex global dataset and turn it into clear, visual trends.
Here is how I worked on this analytical project:
Data Prep & Sorting: Filtered and cleaned a huge raw dataset in Excel, fixing timeline gaps and aligning historical oil price variations properly.
Tracking Global Metrics: Tracked critical high-level data points, such as crude oil peak prices ($130.33), volatility rates (56.22%), and the exact number of war incidents (52).
Analytical Visualizations: Built dynamic trend lines showing monthly and daily crude oil fluctuations, bar charts for sector-wise impacts, and stock market response graphs.
I chose a dark theme with bright teal indicators to give it a modern data terminal look. The dashboard is fully interactive, letting you check the specific impact of different conflict phases instantly.
Tools used: Microsoft Excel (Data Cleaning, Advanced Analytics, Pivot Charts, Dashboard Architecture)
