Product-Recommendation-System gokaycetinn · PARTIAL
This project, developed as part of the CENG 476 Big Data course, presents a complete end-to-end big data solution for real-time product recommendation in the e-commerce domain. It demonstrates how modern distributed technologies can be integrated to ingest, process, and analyze large-scale retail transaction data in a scalable and efficient way.
github.com/gokaycetinn/Product-Recommendation-System · ★ 7 · Forks 0 · Size 14.5 MB
SUMMARY
Technologies 10
Scored 9
Observed 9
Practices 6
Evidence 11
Skips 1
COVERAGE
Analyzed 15 files · 7 commits · 0 API calls
TECHNOLOGIES & DEPTH
Markdown LANGUAGE Depth 70
1 files · PRODUCTION
Python LANGUAGE Depth 70
7 files · PRODUCTION
pip BUILD_TOOL Depth 80
1 files · CONFIGURATION
Apache Kafka MESSAGE_BROKER Depth 77
2 files · CONFIGURATION, PRODUCTION
Flask FRAMEWORK Depth 47
1 files · PRODUCTION
MongoDB DATABASE Depth 65
2 files · CONFIGURATION
NumPy LIBRARY Depth 54
1 files · PRODUCTION
RabbitMQ MESSAGE_BROKER Depth 61
1 files · CONFIGURATION
pandas LIBRARY Depth 47
1 files · PRODUCTION
python-dotenv LIBRARY Depth —
0 files · config only
PRACTICES
documentation · observedautomated_tests · absentcontinuous_integration · absentcontainerization · absentlinting · absentformatting · absent
ACTIVITY & OWNERSHIP
First commit 2025-06-30
Last commit 2025-06-30
Active months 1
Commits 7