{
  "fetched_at": "2026-10-20T11:05:34Z",
  "source_url": "https://www.kaggle.com/competitions/CO2-emissions-forecasting/data",
  "pages": [
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      "name": "data-description",
      "content": "The dataset contains the values of CO2 emissions(mg) since 2001. It's not enough and no missing values in there.\n\n## Files description\n\n*   **train.csv** - the training set\n*   **test.csv** - the test set\n*   **sample_submission.csv** - a sample submission file in the correct format\n*   **CO2emissions.xlsx** - original dataset\n\n## Columns\n\n*   `id` - id of forecasting\n*   `emission_co_mg_forecast` - Values of CO2 emissions forecasting\n",
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      "name": "Description",
      "content": "##What is ML Olympiad?\n\nAn associated Kaggle Community Competitions hosted by TFUGs (Here [Armel Yara](https://www.kaggle.com/armelyara) and [Kimana Misago](https://www.kaggle.com/kimanamisago)) or ML GDEs supported by Google Developers.\n\n\n\n\n\n\n\n## Goal of this competition\nThe aim of this competition is to build a time series model to forecast CO2 emissions based on deforestation in Côte d'Ivoire. \n\nYour work will help civil organisation and national institutions to fight against global warming and in the same time prevent the pollution.\n\n\n#MLOlympiad  AppliedML",
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    {
      "name": "Evaluation",
      "content": "The evaluation metric for this competition is [MAPE]\n(https://en.wikipedia.org/wiki/Mean_absolute_percentage_error). The mean absolute percentage error (MAPE), also known as mean absolute percentage deviation (MAPD), is a measure of prediction accuracy of a forecasting method in statistics.\n\nMAPE is calculated as follows:\n![](https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F3839290%2F171c4b9da42688337330d7e522ebfc59%2Fmape.png?generation=1675298280096656&alt=media)\n\n\nWhere \n##yi is  the actual value \n## ŷ is the forecasted value\n​\n\n## Submission Format\n\n**For every author in the dataset**, submission files should contain two columns: `id` and `emission_co_mg_forecast`. \n\nThe file should contain a header and have the following format:\n```\nid,emission_co_mg_forecast\n0,88798535.2202487\n1,95205432.69541153\n2,81749312.50684434\n3,47907980.89800926\n4,91255169.45161174\n\n```",
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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 \n      in the Private Leaderboard:\n\n          - Upload your code (file or folder, zip compressed) to the Google Drive folder provided\n          - Use this file name format: YourName_YourSurname.zip\n4. Code has to be written in Python\n5. Don't cheat!\n6. Apply yourself!\n7.  Have fun!",
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    {
      "name": "Timeline",
      "content": "February 02, 2023 - Start Date.\n\nMarch 07, 2023 - 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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