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  "fetched_at": "2026-10-19T23:27:25Z",
  "source_url": "https://www.kaggle.com/competitions/blueberry-yield-forecasting-competition/data",
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      "name": "Evaluation",
      "content": "## Evaluation\n\nSubmissions will be evaluated using [Mean Absolute Error (MAE)](https://en.wikipedia.org/wiki/Mean_absolute_error),\n\n![MAE](https://raw.githubusercontent.com/visipedia/iwildcam_comp/master/assets/MAE.png)\n\nwhere each `x_i` represents the predicted target, `y_i` represents the ground truth, and `n` is the number of rows in the test set.\n\n## Submission File\nFor each `id` in the test set, you must predict the target `yield`. The file should contain a header and have the following format:\n\n    id,yield\n    15289,6025.194\n    15290,1256.223\n    15291,357.44\n    etc.",
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      "content": "\n## Competition Rules: Let's Play Fair and Compete Hard!\n\nWe're excited to have you join our competition! To make sure everyone has a fun and fair experience, we've outlined some key rules:\n\n**One Account per Superstar **\n\nYou can only participate with one account. Think of it like your superhero persona – only one per person!\n\n**Team Up or Fly Solo **\n\nWhether you prefer to brainstorm with a team of brilliant minds or tackle the challenge head-on, the choice is yours! Just remember, teams can't exceed [Team Size] members.\n\n**Sharing is Caring (But Only Publicly) ️**\n\nFeel free to share your coding insights and strategies on the competition forums! But remember to keep it public so everyone can benefit from your knowledge.\n\n**Merging Teams? Teamwork Makes the Dream Work! **\n\nNeed to join forces with another team? That's awesome! Just make sure the combined team stays within the size limit and hasn't submitted more entries than allowed for their current size. Remember, mergers have deadlines, so don't wait until the last minute! ⏳\n\n**Submission Limits: Quality Over Quantity **\n\nYou can submit a maximum of [Number] entries per day to showcase your skills. Focus on quality over quantity – your best work will shine! ✨\n\n**Respect the Data: It's Our Playground! ️**\n\nWe've provided some awesome data to help you build your solutions. Use it wisely and ethically! Don't peek at the secret test sets – that's no fun!\n\n**Code Sharing: Open Source Wins! **\n\nFeel free to share your code publicly, as long as it adheres to open-source licensing. This helps everyone learn and grow together!\n\n**Leaderboard: See Who's on Top! **\n\nTrack your progress and see how you stack up against other competitors on the leaderboard. The higher you climb, the closer you get to the prize!\n\n**Fair Play is the Only Way! ‍♀️**\n\nWe want everyone to have a shot at winning. Cheating, hacking, or harassing other participants is a big no-no and will result in disqualification.\n\n**Stay Updated: Knowledge is Power! **\n\nThe competition website is your one-stop shop for all the latest updates, rules, and resources. Check back regularly so you don't miss anything!\n\n**Publicity: Shine Bright Like a Star! **\n\nBy participating, you agree that we might use your name and likeness for promotional purposes (think bragging rights!). But don't worry, we won't do anything you wouldn't be proud of.\n\n**Privacy: We Got You Covered! **\n\nWe take your privacy seriously. We'll only use your information for running the competition and in accordance with our Privacy Policy (which you can find on the website).\n\n**Let's Make History (and Win Prizes)! **\n\nBy entering, you understand that you're agreeing to these rules. Now that we've covered the basics, let's get coding and see who comes out on top!",
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      "content": "**NOTE:** You can now create your own synthetic versions of this dataset by forking and running [this notebook](https://www.kaggle.com/inversion/make-synthetic-blueberry-yield-data/).\n\nThe dataset for this competition (both train and test) was generated from a deep learning model trained on the [Wild blueberry Yield Prediction Dataset](https://www.kaggle.com/datasets/shashwatwork/wild-blueberry-yield-prediction-dataset). (Since this is Playground 3.14, it seems like we need a Blueberry Pie joke here?) Feature distributions are close to, but not exactly the same, as the original. Feel free to use the original dataset as part of this competition, both to explore differences as well as to see whether incorporating the original in training improves model performance.\n\n## Files\n\n*   **train.csv** - the training dataset; `yield` is the target\n*   **test.csv** - the test dataset; your objective is to predict the `yield` given the other features\n*   **sample_submission.csv** - a sample submission file in the correct format",
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    {
      "name": "abstract",
      "content": "\n## 🫐 Calling All Data Science Detectives! ️‍\n\n**Uncover the Secrets of Wild Blueberries with Kaggle!**\n\nWelcome to the **Wild Blueberry Yield Prediction Challenge** , part of the awesome Kaggle Playground Series!\n\n**The Mission:** ️\n\nCan you use your data science superpowers to predict the bounty of wild blueberries? We'll provide a treasure trove of data (a dataset) ️ to help you build a magical model (regression model) ✨ that forecasts the yield of these delicious berries! 🫐\n\n**Why Join the Adventure? **\n\n-   **Level Up Your Skills!** Master regression techniques and gain experience that's berry impressive!\n-   **Help Mother Nature! ** Your insights can lead to sustainable wild blueberry farming!\n-   **Compete and Conquer! ** Test your skills against fellow data whizzes and win big (prizes may be involved)!\n\n**Ready to Dive In?**\n\nHere's the lowdown:\n\n-   **Data Delve:** Explore a dataset filled with clues about wild blueberry yield (environmental factors, etc.) ☀️️\n-   **Model Mastermind:** Craft a top-notch regression model to predict yield based on the data!\n-   **Kaggle Playground:** This user-friendly platform lets you build, test, and perfect your creation!\n\n**Bonus Quest!** Want to add some spice? ️ Fork and run this awesome notebook (link below) to generate your own synthetic wild blueberry datasets!\n\n**Link to Notebook:** [https://www.kaggle.com/inversion/make-synthetic-blueberry-yield-data/](https://www.kaggle.com/inversion/make-synthetic-blueberry-yield-data/)\n\n**Don't miss this chance to combine your data science skills with your love for nature! Join the Wild Blueberry Yield Prediction Challenge and become a wild blueberry whisperer! 🫐**\n\n**Note:** This competition is part of the 2023 Kaggle Playground Series (even though it can run beyond 2023).",
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