https://github.com/mizcausevic-dev/attribution-warehouse-lab
Python FastAPI attribution lab for warehouse journey stitching, channel credit models, SQL contracts, and revenue reconciliation.
https://github.com/mizcausevic-dev/attribution-warehouse-lab
analytics attribution backend data-warehouse fastapi marketing-analytics portfolio python revops sql
Last synced: 1 day ago
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Python FastAPI attribution lab for warehouse journey stitching, channel credit models, SQL contracts, and revenue reconciliation.
- Host: GitHub
- URL: https://github.com/mizcausevic-dev/attribution-warehouse-lab
- Owner: mizcausevic-dev
- Created: 2026-05-15T17:06:08.000Z (18 days ago)
- Default Branch: main
- Last Pushed: 2026-05-17T05:12:02.000Z (16 days ago)
- Last Synced: 2026-05-17T07:25:46.744Z (16 days ago)
- Topics: analytics, attribution, backend, data-warehouse, fastapi, marketing-analytics, portfolio, python, revops, sql
- Language: HTML
- Homepage: https://kineticgain.com/
- Size: 1.27 MB
- Stars: 0
- Watchers: 0
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- Changelog: CHANGELOG.md
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README
# Attribution Warehouse Lab
Warehouse-first attribution lab for **journey stitching, channel credit modeling, weighted allocation, and SQL-visible revenue reconciliation**.
> **What this repo proves**
>
> Attribution gets more useful when it stops being a chart argument and starts being a warehouse contract with inspectable journey rows and model outputs.
## Why this repo exists
Most attribution debates fail for predictable reasons:
- journeys are not stitched cleanly
- sourced and influenced pipeline get mixed together
- leadership sees one favored model but not the alternatives
- SQL logic is hidden behind dashboards instead of being reviewable
`attribution-warehouse-lab` models the warehouse layer directly. It keeps touchpoint journeys, conversion rows, channel credit models, and SQL assets visible in one small local-first repo.
## Screenshots
## What it includes
- FastAPI service with HTML proof surfaces and JSON APIs
- sample warehouse-friendly journey dataset
- first-touch, last-touch, linear, and position-weighted channel allocation models
- SQL model assets for staging, fact, and mart layers
- real PNG screenshots generated from repo-owned HTML scenes
- tests, CI, and one-command local validation
## Local Run
```powershell
cd attribution-warehouse-lab
py -3.11 -m venv .venv
.\.venv\Scripts\python.exe -m pip install -r requirements.txt
.\.venv\Scripts\python.exe -m app.main
```
Open:
- [http://127.0.0.1:5034/](http://127.0.0.1:5034/)
- [http://127.0.0.1:5034/journeys](http://127.0.0.1:5034/journeys)
- [http://127.0.0.1:5034/models](http://127.0.0.1:5034/models)
- [http://127.0.0.1:5034/warehouse](http://127.0.0.1:5034/warehouse)
- [http://127.0.0.1:5034/docs](http://127.0.0.1:5034/docs)
If that port is occupied:
```powershell
$env:PORT = "5038"
.\.venv\Scripts\python.exe -m app.main
```
## Validation
```powershell
.\.venv\Scripts\python.exe -m unittest discover -s tests
.\.venv\Scripts\python.exe scripts\run_demo.py
.\.venv\Scripts\python.exe scripts\smoke_check.py
.\.venv\Scripts\python.exe scripts\render_readme_assets.py
```
## API routes
- `GET /api/dashboard/summary`
- `GET /api/journeys`
- `GET /api/models`
- `GET /api/contracts`
- `GET /api/sample`
## Repo layout
- `app/main.py`
- `app/services/attribution_service.py`
- `warehouse/models/`
- `scripts/`
- `tests/`