Packed Grocery Items Dataset - Expiry Metadata
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1. Expiry Metadata Overview
The Expiry Metadata extension of the Packed Grocery Items Dataset provides comprehensive insights into product shelf life, packaging dates, and expiration tracking for machine learning applications.
Total Metadata Entries
2,857 unique product entries
Metadata Categories
Expiry Date, Manufacture Date, Batch Number
Data Coverage
- Multiple product categories
- Varied expiry date ranges
- Comprehensive tracking information
2. Dataset Distribution Visualization
Dataset Distribution by Set Type
Sample Count Across Dataset Splits
3. Expiry Metadata Considerations
Comprehensive metadata capture strategy includes:
- Manufacture Date Tracking: Recording precise manufacturing timestamps
- Expiry Date Precision: Capturing exact expiration dates for each product
- Batch Number Integration: Unique identifiers for traceability
- Quality Control Metadata: Additional contextual information
4. Data Split Analysis
Comprehensive breakdown of dataset distribution and metadata strategy:
| Set Type | Sample Count | Percentage | Metadata Representation |
|---|---|---|---|
| Training Set | 1,999 | 70% | Primary model training metadata |
| Validation Set | 572 | 20% | Model performance validation metadata |
| Test Set | 286 | 10% | Final model evaluation metadata |
5. Metadata Collection Challenges
Key challenges encountered during expiry metadata collection:
- Varied Packaging Formats: Different labeling standards across products
- Date Format Inconsistencies: Non-standardized date representations
- Partial Information: Some products with incomplete expiry data
- Dynamic Expiry Tracking: Handling frequently changing product information
6. Conclusion
The Expiry Metadata extension provides a robust, comprehensive dataset for advanced machine learning applications focusing on product lifecycle management and quality control.