In my GlobalSupplyChainLogistics project, I analysed a global retail dataset using Python, SQL, and Power BI. I identified a logistics bottleneck, with on-time delivery at 23% against $193M in spend.
I built a six-page Power BI dashboard covering revenue, stock outs, and supplier reliability, with prioritized business recommendations. I also found near-identical competitor pricing, with a 0.99 correlation.
For UPITransactionsAnalysis, I analysed 100K+ transactions and found a 15× higher fraud rate on rooted devices. I built an analytics workflow with Excel, SQL, and Python, then resolved a seven-table Power BI modelling issue using USERELATIONSHIP.
In my HR Analytics—EmployeeAttrition project, I analysed 1,470 employee records and used SQL to identify higher attrition among overtime workers and Sales Representatives. I built a Power BI dashboard to explore attrition trends across departments, roles, and salary bands.
