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  "fetched_at": "2026-10-20T01:08:21Z",
  "source_url": "https://www.kaggle.com/competitions/gses-kaggle-competition-2023/data",
  "pages": [
    {
      "name": "data-description",
      "content": "## Input Attributes\n* MedInc: Median income in block group\n* HouseAge: Median house age in block group\n* AveRooms: Average number of rooms per household\n* AveBedrms: Average number of bedrooms per household\n* Population: Block group population\n* AveOccup: Average number of household members\n* Coord_X: X coordinate\n* Coord_Y: Y coordinate\n\n\n## Target Value (what you should predict)\n* MedHouseVal: Median value of house prices in $1000\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\n## Columns in the Submission\n\n*   `ID` - starting from 0\n*   `MedHouseVal` - the value of predictions",
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    {
      "name": "Description",
      "content": "## Input Attributes\n* MedInc: Median income in block group\n* HouseAge: Median house age in block group\n* AveRooms: Average number of rooms per household\n* AveBedrms: Average number of bedrooms per household\n* Population: Block group population\n* AveOccup: Average number of household members\n* Coord_X: X coordinate\n* Coord_Y: Y coordinate\n\n## Target Value (what you should predict)\n* MedHouseVal: Median value of house prices in $1000\n\n## Notes\n\nWe added noises to the original dataset. You are not supposed to use third-party datasets / per-trained models in this competition.\n\n## Acknowledgements\n\nCohosted by:\nCity Energy Lab, GSES\nSharing City Lab, GSES\nSustainable & Smart City Lab, GSES\nUrban Studies and Design Lab, GSES",
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      "name": "Evaluation",
      "content": "We use Root Mean Squared Error (RMSE) for evaluation. [Wikipedia](https://en.wikipedia.org/wiki/Root-mean-square_deviation).\n\n<img src=\"https://i.stack.imgur.com/osBuF.png\" alt=\"machine learning - Cross Validation Vs Train Validation Test - Cross  Validated\">\n\n<h1>Train Set:</h1>\nThe train set is the dataset provided to participants for developing and training their models. It typically contains a set of input features and corresponding target variables or labels. Participants use this data to build and optimize their machine learning models. The train set is often larger than the test set to provide enough data for effective model training.\n\n<h1>Test Set:</h1>\nThe test set is another dataset provided to participants, but it does not include the target variables or labels. Participants use their trained models to make predictions on this dataset, and then submit those predictions to Kaggle for evaluation. The test set mimics real-world data that the model will encounter after deployment, allowing participants to assess their model's generalization capabilities.\n\n<h1>Sample Submission:</h1>\nThe sample submission file is a template provided by Kaggle, representing the format in which participants should submit their predictions. It usually includes a unique identifier for each data point in the test set and a corresponding column for the predicted target variable. Participants need to fill in the predictions in this file using their trained models.\n\n<h1>What about cross-validation:</h1>\nIt is optional. You can split the train set yourself for cross-validation.",
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    {
      "name": "rules",
      "content": " - You must be a GSES student to join\n - Don't use 3rd party datasets\n - Don't cheat!\n - Apply yourself!\n - Have fun!\n",
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