{
  "fetched_at": "2026-10-20T03:27:12Z",
  "source_url": "https://www.kaggle.com/competitions/ds310fall21house/data",
  "pages": [
    {
      "name": "data-description",
      "content": "## File descriptions\n- train.csv - the training set\n- test.csv - the test set\n- data_description.txt - full description of each column\n- sample_submission.csv - a sample submission ",
      "isPublished": false,
      "hasIsPublished": false,
      "postTitle": "",
      "hasPostTitle": false,
      "mimeType": "",
      "hasMimeType": false
    },
    {
      "name": "Description",
      "content": "## Introduction\n\nIn this project, you need to implement **linear regression models** to predict house prices. You can ONLY use linear regression models (including regularized and non-regularized models). You are not allowed to use non-linear models, such as random forest, neural network, SVM, etc. \n\nYou will be evaluated over the RMSE score on the private leaderboard. Note that you will not see your private leaderboard score before the submission deadline. The project is due on **October 12th**.\n\n## Suggestions\n- This dataset contains missing values. Consider impute the data before training the model.\n- You can try polynomial features and feature interactions to obtain the non-linearity of the model. \n\n## Notice\n- We highly recommend starting this project early as each team can only make five submission attempts every day.\n- Note that using external data is considered plagiarism. You are only allowed to use the data in this competition.\n- We will carefully check the code. Please write some comments and markdown texts to assist us with inspecting the program.\n- Your code needs to be able to reproduce the results on the leaderboard.\n    - Note that the testing dataset is split for the public and private leaderboards. However, you will only see the public leaderboard before the submission deadline. We will use the private leaderboard to decide the final rank of the competition.\n",
      "isPublished": false,
      "hasIsPublished": false,
      "postTitle": "",
      "hasPostTitle": false,
      "mimeType": "",
      "hasMimeType": false
    },
    {
      "name": "Evaluation",
      "content": "## Metric\nSubmissions 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.)\n\n## Submission File Format\nThe file should contain a header and have the following format:\n```\nId,SalePrice\n1758,11011.1\n1759,19011.3\n1760,91111.4\netc.\n```\nYou can download an example submission file (sample_submission.csv) on the Data page.\n\n",
      "isPublished": false,
      "hasIsPublished": false,
      "postTitle": "",
      "hasPostTitle": false,
      "mimeType": "",
      "hasMimeType": false
    },
    {
      "name": "rules",
      "content": "- Using external data is considered plagiarism. You are only allowed to use the data in this competition.\n- Please ensure your submitted code can reproduce results submitted to this competitino.\n",
      "isPublished": false,
      "hasIsPublished": false,
      "postTitle": "",
      "hasPostTitle": false,
      "mimeType": "",
      "hasMimeType": false
    }
  ]
}