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  "fetched_at": "2026-10-20T01:38:35Z",
  "source_url": "https://www.kaggle.com/competitions/galsenaixpaps-delivery-assignment/data",
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      "content": "The dataset contains a history of thousands of deliveries made by Paps. It is anonymized real data from the Paps database and therefore may contain some noise and missing data.\n\n## File descriptions\n1. Training sets\n*   **train_orders.csv** - order details\n*   **train_parcels.csv** - parcel details related to orders\n2. Test sets\n*   **test_orders.csv** - order details\n*   **test_parcels.csv** - parcel details related to orders\n3. Other\n*   **drivers_clean.csv** - information about the drivers\n\n## Data Fields\n\n*   `order_UID_` - an anonymous id unique to a given delivery\n*   `order_client_UID` - an anonymous id unique to a given client\n*   `driver UID_` - an anonymous id unique to a given driver\n*   `parcel UID_` - an anonymous id unique to a given parcel\n*   `size` - size of a given parcel\n*   `receiver_specification_address` - delivery address\n*   `Mission` - Mission type\n  -  `M_A_D` - Provision of a driver to a customer for a given time\n  -   `Messagerie` - Regular delivery where the package is picked up from the customer, goes to the warehouse and is then delivered to the different recipients\n  -   `Tournée` - Concerns clients who have several branches that are regularly visited to pick up or drop off packages",
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      "name": "Description",
      "content": "> Welcome to ML Olympiad hosted by [GalsenAI](https://galsen.ai/) and [Paps](https://papslogistics.com/) 🥳\n\n# What is ML Olympiad?\n\n**ML Olympiads** are associated Kaggle Community Competitions hosted by ML GDEs (Here [Thierno](https://developers.google.com/community/experts/directory/profile/profile-thierno-ibrahima-diop)) or TFUGs, sponsored by Google Developers. \n\n# Goal of the Competition\nThe aim of this competition is to build a multi-class classification model capable of accurately predicting the most suitable driver for one or several given orders based on the destination of the order and the paths covered by the deliverers.\n\nYour work will help Paps to optimize its deliveries by reducing the number of drivers needed during the deliveries.\n\n## Context\nPAPS is a Senegal-based logistics and delivery company that offers customers various logistics services. PAPS is one of the largest last-mile fleets in Senegal, making over 10 million deliveries across its two markets since its inception.\n\nPAPS clients span a range of industries, such as Banking, Telecommunications, and Pharmaceuticals and each customer has specific delivery needs. Assigning the right type of delivery to the right driver quickly becomes a real challenge with such a high number of orders to process.\n\n![Paps deliverers next to their van and making a delivery](https://papslogistics.com/wp-content/uploads/Paps-slider-12-1.jpg)\n\nUntil now, the assignment of orders to the appropriate deliverers was manual, which posed constraints in terms of processing time, scaling, and overall efficiency. Fortunately, the processing history has been maintained and continuously updated which allows the use of advanced analysis and prediction techniques to optimize the process.\n#MLOlympiad\n**Swag prizes for top winners are sponsored by Google Developers.**",
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      "name": "rules",
      "content": "1. Sharing ideas, code, or data with anyone outside of your team is not allowed.\n2. The use of external data or information is not allowed.\n3. Winners have to provide the code used to get their final predictions, that is to say, the one that reflects your final score in the Private Leaderboard:\n- Upload your code (file or folder, zip compressed) to the Google Drive folder provided\n- Use this file name: YourName_YourSurname.zip\n4. Code has to be written in Python\n5. Don't cheat!\n6. Apply yourself!\n7. Most important: 😃 Have fun! 😃",
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      "name": "Evaluation",
      "content": "Submissions for this competition are evaluated using [Mean F1-Score](https://en.wikipedia.org/wiki/F-score) between the predicted and expected answers.\n\nF1 is calculated as follows: \n![F1 formula](https://inside-machinelearning.com/wp-content/uploads/2021/09/F1-Score.png)\n\nWhere:\n![precision and recall formulas](https://miro.medium.com/max/888/1*7J08ekAwupLBegeUI8muHA.png)\n\nThe F1 metric weights recall and precision equally, and a good retrieval algorithm will maximize both precision and recall simultaneously. Thus, moderately good performance on both will be favored over extremely good performance on one and poor performance on the other.\n\n**NOTE:**\n> - True Positive [TP]   = your prediction is 1, and the ground truth is also 1 - **you predicted a positive** and **that's true**!\n- False Positive [FP]    = your prediction is 1, and the ground truth is 0 - **you predicted a positive**, and **that's false**.\n- False Negative [FN] = your prediction is 0, and the ground truth is 1 - **you predicted a negative**, and **that's false**.\n\n## Submission Format\n\n**For every Order in the dataset**, submission files should contain two columns: `Order UID_` and `driver_id`. \n> `driver_id` is between 0 and 79 and represents a mapping with the real id of the drivers.\n\nThe file should contain a header and have the following format:\n```\nOrder UID_, driver_id\n11326944f24cef4fcaaae0e7c391e21efe3d4d7c01af817917c08b842bbfc53a, 0\nea02e4c2c2d7c08037a7ad5c3a0d783a1e9edb162200f4dd5c5d6f7b4c0d070d, 0\n169aa82c0225bf6f449b1b20e0090fa5a581e2f58cc2a17d85a20a58922b36f2, 0\nef73a6526107fd80fd7f87c453798e5a3f02edd7c0bf09e4ec178424f3d315cd, 0\n4f9b60444a55153059a07baa92bf1b80d3638b6c9f2d4ea5bcb8242327690c86, 0\n3545c1b564366d0aa09f678578ee00da3db38fda37fdf8abc5a99f94fec07226, 0\nc9eae2911e82989777188303c543521d18a16cd0f4527253ca7d2a6be759a029, 0\n3ab4e58e675eda0f76fb299fc78b0dd0b94af60b3252ddf0cad83ee072fa69a6, 0\n473512d0b28cc619202cc04893559777a048bf9eaa5479520db129d799db8302, 0\n\n```",
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    {
      "name": "Timeline",
      "content": "- **February 05, 2022** - Start Date.\n\n- **March 05, 2022** - Final Submission Deadline.\n\nAll deadlines are at 11:59 PM UTC on the corresponding day unless otherwise noted. The competition organizers reserve the right to update the contest timeline if they deem it necessary.",
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    {
      "name": "Prizes",
      "content": "> **Swag prizes for the top 3 winners are sponsored by Google Developers.**\n> **The cash prize of 150.000 FCFA is sponsored by PAPS.**",
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    {
      "name": "About Co-Sponsor PAPS",
      "content": "![Paps logo](https://play-lh.googleusercontent.com/ZK05n6eJ8SCAxaLbp2qHROPvmDkgTNUNW8bsTgGCLdKjhxijHxCF2ggc5e6Mxtjbhvon)\nFounded in 2016, **Paps** is a Senegalese company that operates in end-to-end logistics. We offer innovative solutions to companies and merchants to ensure the distribution of documents and packages anywhere in Senegal, in the sub-region, and internationally.\n\n## Services :\n- Instant or scheduled pick-up of parcels and mail;\n- Storage of goods in a secure warehouse that complies with standards;\n- Delivery of insured parcels in a relay point or in last miles;\n- Professional services: management and payment of cash on delivery, providing couriers, returns management, collection, and periodic reporting.\n\nOur services are based on the use of information and communication technologies in order to respond to addressing problems in Africa.\n\nLearn more [here](https://papslogistics.com/).",
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