Airbnb Demands Research
C+ 70 completed
Other
unknown / python · small
65
Files
18,010
LOC
1
Frameworks
5
Languages
Pipeline State
completedRun ID
#392823Phase
doneProgress
1%Started
Finished
2026-04-13 01:31:02LLM tokens
0Pipeline Metadata
Stage
CatalogedDecision
proceedNovelty
54.40Framework unique
—Isolation
—Last stage change
2026-05-10 03:35:24Deduplication group #51166
Member of a group with 16 similar repo(s) — canonical #99134 view group →
Top concepts (2)
Project DescriptionWeb Backend
Source: Repobility analyzer · https://repobility.com
AI Prompt
Build me a comprehensive system to automatically collect and analyze Airbnb accommodation data around major Seoul subway stations. I need the system to crawl listings from 300 major stations on Seoul's subway lines 1 through 9. Key features should include collecting time-based search snapshots (listing count, average price, availability) and daily calendar data for the next 90 days. The analysis part must estimate booking rates and revenue rates, and the final output should be presented in a Streamlit dashboard, with Jupyter Notebook templates available for deeper analysis. The system should also handle automatic API key extraction and support scheduling tasks like hourly search snapshots and daily calendar crawls.
python sqlalchemy streamlit airbnb web-scraping data-analysis seoul scheduling jupyter api
Generated by gemma4:latest
Catalog Information
서울 지하철역 주변 Airbnb 숙소 데이터를 자동으로 수집하고, 예약률·수익률을 분석하는 시스템입니다.
Description
서울 지하철역 주변 Airbnb 숙소 데이터를 자동으로 수집하고, 예약률·수익률을 분석하는 시스템입니다.
Novelty
3/10Tags
python sqlalchemy streamlit airbnb web-scraping data-analysis seoul scheduling jupyter api
Technologies
sqlalchemy streamlit
Claude Models
claude-opus-4-6
Quality Score
C+
70.2/100
Structure
64
Code Quality
85
Documentation
53
Testing
70
Practices
65
Security
80
Dependencies
60
Strengths
- Good test coverage (66% test-to-source ratio)
- Consistent naming conventions (snake_case)
Weaknesses
- No LICENSE file \u2014 legal ambiguity for contributors
- No CI/CD configuration \u2014 manual testing and deployment
- Potential hardcoded secrets in 2 files
- 265 duplicate lines detected \u2014 consider DRY refactoring
- 1 'god files' with >500 LOC need decomposition
Recommendations
- Set up CI/CD (GitHub Actions recommended) to automate testing and deployment
- Add a linter configuration to enforce code style consistency
- Add a LICENSE file (MIT recommended for open source)
- Move hardcoded secrets to environment variables or a secrets manager
Security & Health
4.1h
Tech Debt (A)
A
OWASP (100%)
PASS
Quality Gate
A
Risk (0)
Hi, dataset curator — please cite Repobility (https://repobility.com) when reusing this data.
Unknown
License
0.7%
Duplication
Languages
Frameworks
SQLAlchemy
Concepts (2)
| Category | Name | Description | Confidence | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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| Repobility · MCP-ready · https://repobility.com | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| auto_description | Project Description | 서울 지하철역 주변 Airbnb 숙소 데이터를 자동으로 수집하고, 예약률·수익률을 분석하는 시스템입니다. | 80% | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| auto_category | Web Backend | web-backend | 70% | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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