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

.
├── 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.