https://github.com/fbarffmann/nosql-challenge
Analyzed 28,000+ UK restaurant records using MongoDB and PyMongo. Queried hygiene scores, location data, and customer ratings.
https://github.com/fbarffmann/nosql-challenge
data-analysis data-cleaning database-analysis json mongodb nosql pymongo python restaurant-data
Last synced: about 2 months ago
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Analyzed 28,000+ UK restaurant records using MongoDB and PyMongo. Queried hygiene scores, location data, and customer ratings.
- Host: GitHub
- URL: https://github.com/fbarffmann/nosql-challenge
- Owner: fbarffmann
- Created: 2024-07-17T16:08:06.000Z (almost 2 years ago)
- Default Branch: main
- Last Pushed: 2025-04-13T17:40:38.000Z (about 1 year ago)
- Last Synced: 2025-04-24T01:17:30.272Z (about 1 year ago)
- Topics: data-analysis, data-cleaning, database-analysis, json, mongodb, nosql, pymongo, python, restaurant-data
- Language: Jupyter Notebook
- Homepage:
- Size: 2.88 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# UK Restaurant Data Analysis with MongoDB
Built a NoSQL database using MongoDB to analyze restaurant hygiene scores and customer ratings across the United Kingdom. Ingested a large JSON dataset, created structured queries with PyMongo, and identified trends based on location and hygiene standards.
## Tools & Technologies Used
- Python
- MongoDB
- PyMongo
- NoSQL Databases
- Jupyter Notebooks
## File Structure
```text
.
├── NoSQL_setup_starter.ipynb # MongoDB database and collection creation
├── NoSQL_analysis_starter.ipynb # Querying and analysis
└── Resources/
└── establishments.json # Original dataset (~28,000 records)
```
## Skills Demonstrated
- NoSQL database design and management
- Importing and querying large JSON datasets in MongoDB
- Python scripting with PyMongo
- Filtering and transforming unstructured data
- Performing location-based data analysis
## Key Findings
- Analyzed over 28,000 restaurant records from across the UK.
- Filtered for restaurants in London with hygiene scores above 4.
- Identified restaurants missing location coordinates for cleaning.
- Queried establishments in specific postal codes for targeted analysis.
- Demonstrated flexible querying of NoSQL data using Python.