At AiSPRY, I prepared and cleansed more than 50,000 rows of industrial sensor data for a steel manufacturing energy-consumption project. I identified shift- and equipment-level inefficiency patterns that informed downstream recommendations.
I built and validated a regression model to forecast energy-demand peaks, achieving an R² of approximately 0.85. The output informed load-scheduling decisions presented to operations stakeholders.
I also developed visualizations of energy KPIs across production shifts and documented the preprocessing, model rationale, validation results, and recommendations for project handover. As an AI Content Development Intern at AiSPRY, I designed three course modules and analyzed learner engagement data to identify structural drop-off points.
In personal projects, I independently built five interactive dashboards covering sales, HR, financial performance, customer churn, and e-commerce. I work with SQL, Python, Power BI, Tableau, and SAP Analytics Cloud, and bring an agriculture background through my B.Sc. in Agriculture.
