MurtazaMajid/Campbells-AI-and-Marketing-Hub
End-to-end AI marketing intelligence platform: predicts customer churn at 84% AUC, segments 1,500+ customers via RFM KMeans clustering, runs ABSA sentiment analysis, and auto-generates personalised SMS / email / push notifications using LLaMA 3.3-70B via Groq.
SUMMARY AI summary by gpt-5-mini
An end-to-end AI marketing system built for a real restaurant (Campbell’s) that turns transactions, reviews and menu data into actionable customer intelligence. It performs RFM-based customer segmentation (KMeans), a two‑tier churn risk model (XGBoost + rules), aspect-based sentiment analysis (TF‑IDF + logistic regression), behavioral profiling (7 features) and generates personalized re‑engagement messages via an LLM (Groq LLaMA 3.3). Who uses it: restaurant operators, marketing analysts, and data engineers who need to identify at‑risk customers, understand sentiments, prioritize outreach, and automate tailored messaging. Key features: live deployed stack (FastAPI backend, Supabase Postgres, React frontend on Vercel, Railway hosting), interactive dashboard and API endpoints for segmentation/churn/sentiment/customer profiles, pickled ML pipeline, and a dataset of 12,545 transactions across 2,041 customers with labeled reviews and menu data. Tech highlights: Python, FastAPI, React, XGBoost, scikit‑learn.
DETECTED Detected AI stack
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Total AI relevance score: 75 / 100
Position among AI repos in the same language
We track 972 Jupyter Notebook repos, of which 720 score 40 or above on AI relevance. 93 have more stars than this one (top 9.7%), and 32 score higher (top 3.4%).
Owner
Data Science Fresh Graduate. Building deployed end-to-end ML systems using NLP, computer vision, and time-series. Currently open to Data / ML / AI roles.
Dates
| Created on GitHub | 2026-04-23 |
| Last push | 2026-05-09 |
| First seen here | 2026-05-09 |
| Last fetched | 2026-08-17 16:13 |
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