Smart Device Usage & Wellness Strategy

In this project, I explored how women use smart wellness devices by analyzing Fitbit activity data. Using Python and the Pandas library, I cleaned and analyzed daily activity logs from over 30 users across a 31-day period. The analysis revealed low physical engagement and high sedentary behavior among users — a key insight for a company like Bellabeat, which targets women interested in wellness and self-care.

Based on these behavioral patterns, I developed a marketing recommendation focused on empathetic design and long-term wellness habits. The strategy suggests Bellabeat position itself not just as a fitness tracker, but as a supportive wellness companion for women seeking sustainable lifestyle changes.

Tools Used: Python, Pandas, Seaborn, Matplotlib  

Key Skills:Data cleaning, behavioral segmentation, strategic recommendation  

Delivery Optimization Using Six Sigma DMAIC

In this business analysis project, I applied the Six Sigma DMAIC framework to improve on-time delivery performance for a logistics system. The original delivery success rate was 81%, and the goal was to raise it to 98% within three months.

Using process mapping and root cause analysis, I identified key issues such as inconsistent routing, lack of proactive communication, and misaligned performance metrics. Data was analyzed using Excel and SQL to uncover trends, delays, and failure points. Control charts and Pareto analysis helped prioritize actions.

As a result of process improvements and monitoring plans, the delivery performance improved significantly. This project demonstrated my ability to combine process thinking with data analysis to solve real operational problems.

Tools Used: Excel, SQL, Control Charts, Pareto Analysis  Key Skills: Six Sigma  Process Mapping, Root Cause Analysis, KPI Monitoring, Continuous Improvement