Projectssmartsim-analytics
Simulation Analytics / Full-Stack Data Platform

SmartSim Analytics

A full-stack simulation analytics platform for CSV/JSON exports, deterministic Python analysis, KPIs, anomaly detection, trend and stability checks, and PDF reporting.

ReactTypeScriptNode.jsExpressMongoDBPythonViteChartsPDF reporting
View GitHubLive Demo: Coming soonAPI Demo: Coming soon
SmartSim Analytics simulation analytics preview showing CSV/JSON input, Python analysis, dashboard output, and reporting

GitHub

Repository available

Source code and project documentation

Live Demo

Coming soon

API Demo

Coming soon

CI / build

GitHub Actions CI configured

Problem

What needed to be solved

Simulation exports can be difficult to review manually because the data is spread across rows, signals, and multiple derived measures. Teams need a clear way to validate the input, identify anomalies, understand stability, and present findings in a format that is easy to review.

Solution

How the project approaches it

I structured SmartSim Analytics around a React frontend, a Node/Express API, MongoDB persistence, and a deterministic Python analyzer. The backend validates uploads, confines analysis to uploaded files, and runs a bounded Python script that calculates KPIs, detects anomalies, evaluates trend and stability, and returns versioned JSON for the dashboard.

Key features

What the project includes

CSV/JSON simulation data ingestion
Python-powered deterministic analytics
KPI extraction and anomaly detection
Trend and stability analysis
React dashboard with charts
PDF reporting workflow
Node/Express API with validation
MongoDB-backed project storage
Bounded Python execution for safer analysis
Sample data and recruiter-facing documentation
GitHub Actions CI

Architecture / Technical decisions

Implementation choices worth reviewing

Separated the React dashboard, Node/Express API, MongoDB persistence, and Python analyzer so upload, storage, and analytics responsibilities stay explicit.
Kept the Python analysis deterministic and bounded to uploaded CSV/JSON files instead of relying on generated or external data.
Returned structured JSON from the analyzer so dashboard rendering, KPI review, anomaly checks, and PDF reporting share the same contract.

Validation / Quality

How I made the work reviewable

Upload validation before analysis
Deterministic KPI, anomaly, trend, and stability outputs
Sample data and recruiter-facing documentation
GitHub Actions CI listed in the project scope

What I learned

Technical takeaway

I learned how important it is to define stable input/output contracts, keep analytics deterministic, and treat execution boundaries as part of the product rather than an afterthought.

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