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Real work.
Real results.

Three projects showing exactly what Luma Analytics delivers — the insight, the numbers, and what action was taken because of them.

Project 1 — Meta Ads ROAS (Live)
Small Business · Live Project · Real Data

Meta Ads ROAS Prediction Dashboard

A live end-to-end analytics project built on real Meta/Facebook ad data. A Random Forest model predicts return on ad spend per campaign — actual vs predicted ROAS shown side by side in a live Metabase dashboard connected to PostgreSQL.

Python scikit-learn Random Forest pandas PostgreSQL psycopg2 Metabase
Data sourceMeta Ads Manager
PredictsAd return on spend
OutputLive dashboard
Real Live Meta ad data
29 Campaign features modelled
Live Metabase dashboard

What was built

Real ad data, not samples
Started with a raw Meta Ads Manager export — messy, real-world data — and cleaned it into something actually usable.
Predicted which campaigns would win
Built a model that looks at how a campaign is performing now and forecasts its return — so you know where to put budget before it's too late.
Everything updates automatically
Predictions feed directly into a live database — no manual exports, no spreadsheets. The dashboard always reflects the latest numbers.
A dashboard a team can actually use
Actual vs predicted return shown side by side — built so a marketing team could open it every morning and know exactly where to focus.

Dashboard Preview

Meta Ads ROAS Intelligence — AM Analytics
Total Ad Spend
$12,480
Total Revenue
$54,210
Actual ROAS
4.3×
Predicted ROAS
4.6×
↑ Model forecast
Actual vs Predicted ROAS by Campaign
Summer Sale
5.8×
↳ predicted
6.1×
Retargeting
4.3×
↳ predicted
4.5×
Awareness
2.0×
↳ predicted
1.7×
Top Predictors of ROAS (Feature Importance)
CTR
0.31
Spend
0.22
Video views
0.17
Impressions
0.11
Clicks
0.07

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