Datasets:
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README.md
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license: mit
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---
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license: mit
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language:
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- en
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tags:
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- shipping
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- logistic
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- analytics
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---
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# Shipping Events EU Dataset
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## Overview
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This dataset contains simulated yet realistic shipment tracking records from various European locations. It reflects common logistics patterns, including parcel movements, delivery statuses, geographic attributes, carriers, delivery attempts, region types, transit performance, and service-level indicators.
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It is designed to be suitable for machine learning, analytics, research, and operational benchmarking. The dataset structure is aligned with real-world logistics data models and includes temporal, geographic, categorical, and status event features.
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Each row represents a shipment with key delivery and transit attributes.
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---
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## Key Features
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| Column Group | Description |
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|--------------------|-------------|
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| Order Information | Shipment ID, customer reference, carrier, service type |
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| Location Details | Origin, destination, country, coordinates, region type |
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| Time Attributes | Creation date, transit timestamps, SLA windows |
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| Status Events | Delivery status text, codes, exception flags |
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| Performance Fields | Time in transit, attempts, cost, distance, delays |
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---
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## Example Columns
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- `order_id`
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- `carrier`
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- `service_type`
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- `dest_city`, `dest_country`
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- `transit_time_hours`
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- `has_exception_flag`
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- `route_distance_km`
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- `shipping_cost`
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- `last_status_message`
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---
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## Usage
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### Load with Pandas
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```python
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from datasets import load_dataset
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ds = load_dataset("happyhackingspace/shipping-events-eu", split="train")
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df = ds.to_pandas()
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df.head()
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```
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---
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## Notes
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- Dataset is synthetic but modeled after realistic logistics structures.
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- Includes occasional missing values, minor inconsistencies, and noisy entries to mimic real operational data.
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- No personal or sensitive information is included.
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---
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## License
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This dataset is free to use for research, analysis, and educational purposes.
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For feedback or contributions, please use the repository discussion page.
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