Nafiis Farhan

I've built interactive dashboards and predictive models that turn data into practical business insight, using Python, SQL, Excel, Power BI, Looker, and Streamlit.

For a retail sales dashboard, I processed roughly 10,000 transactions and created 20 pivot tables and 22 visualizations to monitor sales, profit, targets, regional performance, and monthly trends.

In my BNSP Data Scientist projects, I developed and deployed machine learning applications for heart disease risk and bike-rental demand. My tuned Random Forest heart disease model achieved a ROC-AUC of 0.91, while my XGBoost bike-rental model achieved an R² of 0.95 on test data.

At Halofina's MSIB program, I designed web-platform UI/UX prototypes and transaction-data visualization features, iterating with team feedback. I also led an AIESEC local project, coordinating an international team, tracking daily progress, and keeping work on schedule.