Thomas-George-T/Ecommerce-Data-MLOps
End-to-End deployment of E-commerce customers segmentation using Clustering Machine learning algorithms in Google Cloud Platform and MLOps Tools
This project helps e-commerce businesses understand their customers better by organizing them into distinct groups. It takes your customer transaction data, including purchase history and demographics, and identifies hidden patterns to create detailed customer segments. This is ideal for marketers, sales strategists, and business analysts who want to personalize marketing efforts and improve customer experiences.
No commits in the last 6 months.
Use this if you need a repeatable and automated way to segment your e-commerce customer base for targeted marketing and personalized service.
Not ideal if you're looking for a simple, one-time data analysis without the need for continuous updates or integration into a larger MLOps pipeline.
Stars
20
Forks
10
Language
Python
License
—
Category
Last pushed
Jun 05, 2024
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/mlops/Thomas-George-T/Ecommerce-Data-MLOps"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Related tools
eltonvaliyev2008-hub/bank-customer-segmentation
Bank customer segmentation using KMeans clustering + Random Forest | FastAPI deployment | 98.3% accuracy
savinaysingh7/customer-segmentation-shopping-trends
End-to-end customer segmentation pipeline using K-Means clustering on 3,900 retail transactions...
Khiladi-786/customer-segmentation-dashboard
Interactive ML dashboard for customer segmentation using K-Means clustering. Features real-time...
beratmertkayacan/HealthOps-Analysis
HealthOps: AI-driven patient segmentation and hospital efficiency analysis using K-Means...
yaswanth-AIML/Customer-Segmentation
This project demonstrates **customer segmentation using unsupervised learning (KMeans...