Releasedv1.0.0Forecasting · Analytics

Enterprise Business Analytics & Forecasting Platform

Business forecasting platform with chronological evaluation, baseline comparisons, and decision-support API packaging.

Chronological KPI forecasting with baselines vs LightGBM, MAPE/RMSE reporting, and FastAPI decision support.

Interactive Demo GitHub Metrics Architecture API Docs Presentation README

Problem

Plans fail when forecasts are opaque

Spreadsheets hide baseline comparisons

Random splits leak future information

Decision support needs API packaging

Solution

Classical DS forecasting with production packaging

Chronological evaluation discipline
Baselines vs primary reported honestly
Horizon forecasts via FastAPI

Pipeline

Interactive system flow

Selected stage

KPI Panel

Store × SKU daily demand panel.

Architecture

System architecture

Download

Interactive Demo

Use the real system

Start with Run Example — one click fills a realistic payload and calls the live FastAPI via this site's proxy.

StoreSKUHorizon (days)14

Forecast chart appears after a successful API call.

// Forecast payload

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Performance

Measured evaluation results

Numbers below match this project's evaluation reports. Dataset scope, baselines, and limitations are documented under Integrity.

Primary MAPE

0.055

Baseline 0.074

Primary RMSE

3.17

Baseline 4.20

Primary MAE

2.46

Baseline 3.29

Comparison chart · source: project reports/metrics

Metric	Primary	Baseline
Primary MAPE	0.055	0.074
Primary RMSE	3.17	4.20
Primary MAE	2.46	3.29

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Engineering Quality

Production packaging signals

Chronological split

Leakage-safe holdout

MLflow

Experiment logging

Docker

Dockerfile

GitHub Actions

Dashboard

Performance pack

Docs

Engineering docs 00–28

Integrity

Engineering decisions & limitations

Chronological splits — no random leakage
Seeded synthetic panel — methodology demo, not live ERP
Primary beats baseline on validation before test report
Recursive multi-step at serve; one-step tabular eval documented
Real metrics only after training runs

Repository

Source of truth

README Architecture Presentation Dashboard Repository Release v1.0.0

Presentation

Hiring presentation pack

Presentation pack Performance dashboard Architecture page

API Docs

Execute live FastAPI contracts

// Response JSON appears here

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README

Source documentation

Open project README →

About Engineer

Mohammad Ahmadian

AI / Machine Learning Engineer

Production-oriented AI/ML systems — evaluation, APIs, and honest limitations. Turkey (GMT+3).

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