Projetssmartsim-analytics
Analyse de simulation / plateforme full-stack de donnees

SmartSim Analytics

Une plateforme full-stack d'analyse de simulation pour les export CSV/JSON, l'analyse Python deterministic, les KPI, la detection d'anomalies, les controles de tendance et de stabilite, et les rapports PDF.

ReactTypeScriptNode.jsExpressMongoDBPythonViteChartsPDF reporting
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

Probleme

Ce qu'il fallait resoudre

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

Comment le projet y repond

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.

Fonctionnalites clefs

Ce que le projet comprend

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 / decisions techniques

Choix d'implementation a examiner

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 / qualite

Comment j'ai rendu le travail evaluable

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

Ce que j'ai appris

Enseignement technique

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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Plus d'etudes de cas

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