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  "fetched_at": "2026-10-19T19:05:39Z",
  "source_url": "https://www.kaggle.com/competitions/airbnb-madrid-ironhack/data",
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    {
      "name": "data-description",
      "content": "Data source [here](http://insideairbnb.com/get-the-data.html)\n\nCheck the data fields descriptions in the project [readme file](https://github.com/Ironhack-Data-Madrid-Enero-2022/w7-Kaggle_competition).\n\n## Files\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*   **metaData.csv** - supplemental information about the data\n\n## Columns\n\n*   `example_id` - definition of example_id\n*   `feature_1` - definition of feature_1\n*   etc.",
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    {
      "name": "Description",
      "content": "# IronAirBnb\n**FOR IRONHACK MADRID DATA STUDENTS ONLY**\nBuild a predictive model for AirBnB in Madrid.\n\n![img](https://nbviewer.org/github/Ironhack-Data-Madrid-Enero-2022/w7-Kaggle_competition/blob/main/img/Airbnb-inventory-travel-vaccinations.jpg)\n\n\nThe goal of this competition is the prediction of the price of AirBnB accommodations in Madrid based on their characteristics (number of bedrooms, bathrooms, property type, etc.), putting into practice all the machine learning techniques you know.\n\n\n[![IMAGE ALT TEXT](https://images.squarespace-cdn.com/content/v1/551c68ffe4b00eb221731761/1603406087509-2PU9KQLGR1WODROPZEJH/image-asset.jpeg?format=1000w)](https://www.youtube.com/watch?v=KBA3Blgzsl8)",
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    {
      "name": "Evaluation",
      "content": "# Evaluation\nThe evaluation metric chosen for this competition is the RMSE ([Root Mean Squared Error](https://www.statisticshowto.com/probability-and-statistics/regression-analysis/rmse-root-mean-square-error/)):\n\n## Submit results\n- Submissions are limited to **5 times a day**.\n- The minimum submissions required are 1 per day.\n- The test set is divided into a public part (with which the public leaderboard is calculated, accessible during the competition) and another private part, with which the final positions are calculated, after the end of the competition.\n- Predictions will be sent in the format indicated in the sample_submission.csv file in the data section.\n",
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    {
      "name": "rules",
      "content": "- Don't cheat!\n- Apply yourself!\n- Have fun!\n\n**For Ironhack Madrid Campus students only**",
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    {
      "name": "Timeline",
      "content": "## **This competition will finish 24/02/2022 23:59 AM (GMT+1)**\n\n![time](https://cdn.quotesgram.com/img/40/93/1377625221-minions-architects-other.jpg)",
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    {
      "name": "Prize",
      "content": "You will receive a book 👏 and knowledge, a bunch of it. And a little surprise.\n\n![prize](https://nbviewer.org/github/Ironhack-Data-Madrid-Enero-2022/w7-Kaggle_competition/blob/main/img/trip_surprise.jpg)",
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    {
      "name": "Requirements",
      "content": "![image](https://media.makeameme.org/created/yes-these-requirements.jpg)\n\n- Train a minimum of 4 different models\n- Perform  Feature Extraction and Engineering techniques\n- Documentation needed to reproduce the code\n- The code in .py and/or .ipynb files that allows to reproduce the exercise.\n- Functions must be in modules. ;)(src folder with .py files)\n- The Readme must contain a summary of the machine learning tools and algorithms\nand the results or the score obtained with each of them.\n- A slide (just one) That show an overview of your project)",
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