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Python — Sales Forecasting & Data Analytics Tool
The network for creativity
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Connect with clients, get discovered, and run your business 100% commission-free
Creatives on Contra have earned over $150M and we are just getting started
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Just came across this website and really liked the way they’ve combined clean design with immersive interactions.
The smooth animations, typography, transitions, and product storytelling make the whole experience feel premium without being overwhelming.
Website: otsuka-air.jp
What stood out to me:
• Smooth and thoughtful interactions
• Clean visual hierarchy
• Strong product storytelling
• Minimal yet engaging UI
• Great use of motion throughout the experience
Would love to know what you guys think about this design approach.
What’s your favorite detail on this site? 👀
Amazing!
Prism Analytics — From Complex Data to Clear Decisions
PRISM ANALYTICS
From Complex Data to Clear Decisions.
The Problem
B2B teams often have plenty of data, but finding the right business signal quickly can be difficult.
The Solution
I designed a premium analytics dashboard that brings Revenue, Active Users, Conversion Rate, Average Order Value, Revenue Trends, Recent Sales, and User Engagement into one clear workspace.
My Approach
I focused on information hierarchy, data visualization, fast scanning, consistent components, and a clean responsive UI — turning complex metrics into an interface that feels simple to use.
The Result
A focused SaaS dashboard experience that helps founders, product teams, and decision-makers understand performance faster and move from data to action with less friction.
Designed for real-world B2B SaaS products — not just a visual concept.
UI/UX • Product Design • SaaS • Dashboard • Data Visualization
Live Prototype →
https://cycle-drop-04018066.figma.site/
Building a SaaS product and need a dashboard that makes complex data easier to understand?
Let’s work together →
Same three KPI cards and revenue chart repeated on laptop and dual monitors is the real test of a dashboard - if it still reads clearly at that density, the hierarchy is doing its job.
From Airflow DAGs to a lightweight dashboard-only mode.
I developed an automated ETL pipeline for London weather data. The pipeline handles Extract (REST API polling), Transform (data cleaning & normalization), and Load (SQLite with timestamps). The dashboard shows Current Temp 23.3°C, PM2.5 4.30 µg/m³, historical trends, and raw data table.
Key decisions: switched from full Airflow setup to a lightweight Streamlit app for faster deployment and lower resource usage, while keeping the code clean and reproducible.
Repo structure: dags / src / dashboard / data / scripts - ready to be used as a template for any weather/IoT data project.
