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Analyzed CDC diabetes health indicators dataset
– Analyzed 253k+ survey records to uncover risk factor patterns; built Decision Tree models with 5-fold cross-validation, achieving 82% classification accuracy.
– Optimized model performance through hyperparameter tuning, improving recall of positive diabetes cases by 15%, supporting more accurate early-risk detection insights.
– Tech Stack: Python, Pandas, NumPy, Scikit-learn, Seaborn, Matplotlib, Imbalanced-learn
The network for creativity
Join 1.25M professional creatives like you
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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Currently exploring a book design focused on making complex information easier to understand through visual language. Playing with typography, data visualization, and graphic systems to give the subject a more editorial feel.
This is a trully elegant and impressive design concept.
I love it.
AI Rent & Arrears Follow-Up Automation: Property Management Dashboard
A working demo of an arrears management dashboard for property managers. It detects overdue tenants, ranks who to follow up first, and drafts safe, factual tenant messages from stored data.
Chasing late rent is slow and inconsistent. Managers often don't know whom to contact first, and messages are written from scratch each time. I built a demo system that turns rent and payment data into a clear daily workflow:
Rent data → arrears detection → priority → follow-up message → escalation → resolution
What it does:
Dashboard: total rent due, collected, outstanding, overdue tenants, high-risk accounts and follow-ups due today.
Arrears queue: every overdue tenant with property, amount, days overdue, priority and a suggested action.
AI Arrears Prioritization: automatically ranks who the manager should contact first, based on days overdue, amount owed and past payment behaviour.
Account analysis: shows the amount outstanding, days overdue, payment history, communication log and a recommended next action for any tenant.
Tenant message generation: drafts a polite reminder, follow-up or escalation message matched to the account's stage.
Manual stage control: the manager moves each account through Reminder, Follow-up, Escalation and Resolved.
Tech stack: Python, Flask, SQLite, HTML, CSS, JavaScript, with optional local AI through Ollama and a rule-based fallback. It needs no paid APIs and runs fully offline.
Demo data: 5 properties, 20 units and tenants, six months of payment history and prior follow-up records, so the dashboard looks like a live system.
Honest framing: I'd label it a "working demo" or "prototype" rather than a production system. It uses seeded sample data and has no real SMS or email sending or login yet. Those would be the natural next steps for a client.
📊 Turning Data Into Insights
Sharing one of my recent Power BI dashboard projects.
I worked on transforming raw data into an interactive dashboard with a focus on:
• Data cleaning & transformation
• KPI tracking
• Interactive visualizations
• DAX calculations
• Trend & performance analysis
🛠️ Tools: Power BI • Power Query • DAX • Excel
Still learning, building, and improving with every project. 🚀
#PowerBI #DataAnalytics #DataVisualization #DAX #BusinessIntelligence #DataAnalyst #Analytics #Dashboard
