Analysis workflow
The diagrams summarize the documented upload, KPI calculation, anomaly review and reporting workflow.
SmartSimAnalytics
My contribution
Where I focused.
- Built interactive dashboards for simulation results and engineering indicators.
- Connected the web workspace to backend and Python analysis services.
- Automated reporting to make simulation results easier to interpret.
01 / The problem
Simulation teams receive dense CSV and JSON exports, but extracting stability, anomalies and useful comparisons requires repeated manual analysis.
02 / My response
I connected a responsive React workspace to an Express API and Python analytics service. The system validates uploads, calculates KPIs, detects anomalies and turns the output into interactive charts and reports.
What changed
01
Automated signal KPI calculation
02
Detected anomalies in uploaded datasets
03
Combined analysis, visualization and reporting
A quick product walkthrough
Evidence & verification
Service responsibilities
The architecture view reflects the existing case study's React, Express, Python and MongoDB responsibilities.
Keep files, analysis and findings connected to the same project.
Each output answers a different question about the same simulation.
Engineering decisions
The choices behind the interface.
Python analytics service
Kept numerical processing close to pandas, NumPy, SciPy and scikit-learn while the product layer remained TypeScript-based.
Project-based workspace
Grouped files, analyses and reports by engineering project to preserve context.
Explainable results
Paired charts with KPIs and recommendations so findings are useful beyond a single visualization.
Stack & capabilities
ReactTypeScriptExpressMongoDBPythonRechartsDocker
View source on GitHub ↗