PORTFOLIO — CASE BY CASE

Joanna Elendu

I build practical tools where machine learning meets real people. Each project below is pinned up like evidence: the problem, what was built, the judgment call, and the honest status.

THE BOARD

Three investigations, one board

APPLIED ML
CASE 001 — LIVE Emotion-aware music for Alzheimer's care WEBCAM → MOOD → MUSIC · HANDS-FREE
PROCESS AUTOMATION
CASE 002 — LIVE Remittance reconciliation, automated 4 CHECKS · REFRESHES ITSELF EVERY 30 MIN
ANALYTICS & RESEARCH
COMING SOON Analytics & research cases EVIDENCE BEING GATHERED
CASE FILE 001 — APPLIED ML

Emotion-aware music for Alzheimer's care

happy 99% happy neutral sad 😊 HAPPY ♪ playing happy music — hands-free
BROWSER DEMO — READS THE FACE, PLAYS THE MUSIC
THE PROBLEM Alzheimer's patients can't operate a music app. Comfort has to reach them hands-free.
THE BUILD A webcam reads the face; matching music plays itself. No buttons, ever. DESKTOP APP + BROWSER DEMO
THE CALL Emotions flicker frame to frame. The music waits for a sustained mood — steady beats twitchy.
STATUS Working prototype. Live demo runs in any browser. Not clinically tested.
PYTHON · OPENCV · ONNX · FACE-API.JS
MORE CASE NOTES +

The system watches through a webcam, finds the face, and classifies its expression with an emotion model from the FER-2013 family. Raw predictions flicker, so the mood is decided by a majority vote over a sliding window, with a minimum hold time before the music may change — then tracks crossfade, never cut.

It exists in two forms: a hands-free Python desktop app (caregivers drop the patient's favourite songs into mood folders — familiar music works best) and a browser demo that runs entirely on your device and synthesizes its music live, so anyone can test it without installing anything.

The project began during a fellowship at Samsung Electronics America, exploring facial-emotion recognition for Alzheimer's care.

CASE FILE 002 — PROCESS AUTOMATION

Remittance reconciliation, automated

The reconciliation workbench: a duplicate payment row is highlighted and zeroed, and the payment column ties out to the remit total
LIVE WORKBENCH — A DUPLICATE PAYMENT CAUGHT AND ZEROED
THE PROBLEM The billing feed moves every cycle. Tying it out by hand meant hours in a giant spreadsheet.
THE BUILD A tested SQL pipeline runs four checks per account and color-codes every verdict. LIVE APP · NEW DATA EVERY 30 MIN
THE CALL Only provable duplicates get repaired. Everything else goes to a human — never force a match.
STATUS Live and self-running. 43 tests on every build. Data is 100% synthetic.
PYTHON · DBT · DUCKDB · STREAMLIT
MORE CASE NOTES +

Generalized from a real workflow: utility-billing remittances pulled from SQL Server, reconciled by hand in Excel while the book changed underneath — payments posting, services cancelling, adjustments landing. This rebuild uses 100% synthetic, self-generated data and no employer code or rules.

Per account, every cycle: three detail-to-summary sum checks plus an R-C identity check against what the export claims. When duplicate dollar amounts inflate a column, the engine zeroes the duplicate occurrences — never a real amount, never a delete — until detail ties to summary; every repair carries a SOX-style adjustment note for manager approval, and anything unfixable is routed to manual review.

The pipeline runs itself: every 30 minutes a GitHub Action appends a new synthetic remit cycle, reruns all 18 dbt models and 43 tests, commits the refreshed warehouse, and the public app redeploys. The app includes a workbench that replays any account's reconciliation step by step, and a pipeline page that renders the lineage graph and test results from dbt's own artifacts.

ABOUT

The investigator

WHO Business analyst who builds practical tools where data meets real people. ANALYSIS · AUTOMATION · APPLIED ML
CREDENTIALS M.S. Analytics — Georgia Tech, in progress. MBA (MIS) — LAMAR · B.S. HEALTH SCIENCES — HERZING
TRAIL SO FAR A decade from healthcare operations to business systems to applied analytics. CONSTELLATION ENERGY · RES · CVS HEALTH · SAMSUNG FELLOWSHIP
SQL · PYTHON · POWER BI · TABLEAU · SALESFORCE · NETSUITE · EXCEL
FULL DOSSIER +

Business Analyst — Retail Operations, Constellation Energy (contract), Houston · 2025–2026. Billing application workflows for commercial energy accounts: requirements, UAT, root-cause analysis, and Python automation for remittance reconciliation. UAT work contributed to a ~30% reduction in billing errors.

IT Business Systems Analyst, Resource Environmental Solutions · 2024–2025. Salesforce and NetSuite configuration, Power BI validation, requirements for a OneStream implementation (working with CFO, VPs, Directors), and Python pipelines feeding Microsoft Fabric.

Technical Solutions Analyst, GoDaddy · Asurion · Lexia Learning · 2022–2024. Frontline software and systems support; knowledge-base documentation that cut repeat escalations by 15%.

Earlier: Operations Supervisor at CVS Health (promoted within 6 months; proposed the store's dedicated COVID-vaccination kiosk), and Healthcare Support Administrator at Atwell Home Healthcare — EHR administration (EpicCare Home Health, CradleMRx, Kantime, OASIS) and a patient census grown from 10 to 50 in under a year.

Education: M.S. Analytics, Georgia Institute of Technology (in progress, 2027) · MBA in Management Information Systems, Lamar University (2023) · B.S. Health Sciences, Herzing University (2021). Machine-learning fellowship at Samsung Electronics America — where Case 001 began.

CONTACT

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Work

About

Business analyst who builds practical tools where data meets real people — analysis, automation, and applied machine learning. A decade of experience across healthcare operations, technical support, and business systems (Constellation Energy, Resource Environmental Solutions, GoDaddy/Asurion/Lexia Learning, CVS Health, Atwell Home Healthcare).

Education: M.S. Analytics, Georgia Institute of Technology (in progress, 2027) · MBA in Management Information Systems, Lamar University (2023) · B.S. Health Sciences, Herzing University (2021). Machine-learning fellowship at Samsung Electronics America.

Tools: SQL, Python, Power BI, Tableau, Microsoft Fabric, Salesforce, NetSuite, Excel (Power Query, VBA), EHR systems (EpicCare Home Health, CradleMRx, Kantime, OASIS).

Contact

joan_elendu@yahoo.com · linkedin.com/in/joanelendu · github.com/jelendu