{
  "fetched_at": "2026-10-19T23:10:36Z",
  "source_url": "https://www.kaggle.com/competitions/cro-ds-test-condominium-price-prediction-th/data",
  "pages": [
    {
      "name": "data-description",
      "content": "## 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",
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    {
      "name": "Description",
      "content": "##Practice Skills\nCreative feature engineering\nAdvanced regression techniques like random forest and gradient boosting",
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      "hasIsPublished": false,
      "postTitle": "",
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    {
      "name": "Goal",
      "content": "It is your job to predict the sales price for each house. For each Id in the test set, you must predict the value of the SalePrice / square meter (BAHT).\n\n",
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    {
      "name": "rules",
      "content": " - Don't cheat!\n - Apply yourself!\n - Have fun!",
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      "hasIsPublished": false,
      "postTitle": "",
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    {
      "name": "Evaluation : RMSE (Log)",
      "content": "Metric Submissions are evaluated on [Root-Mean-Squared-Error (RMSE)](https://en.wikipedia.org/wiki/Root-mean-square_deviation) between the logarithm of the predicted value and the logarithm of the observed sales price. (Taking logs means that errors in predicting expensive houses and cheap houses will affect the result equally.) ",
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    {
      "name": "Submission File Format",
      "content": "The file should contain a header and have the following format:\n\n```\nId,Predicted\n1461,10.1\n1462,14.1233\n1463,17\n```\n",
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      "hasIsPublished": false,
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