{
  "fetched_at": "2026-10-20T03:29:54Z",
  "source_url": "https://www.kaggle.com/competitions/dsi-nyc-reg/data",
  "pages": [
    {
      "name": "rules",
      "content": "Normal ones!",
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      "content": "There are three files:\n\n**train.csv** -- this data contains all of the training data for your model. It is the same data found in the Classification Challenge so you may use the training set from either challenge's page for both.\nBoth target variables (SalePrice and Sale Condition) are removed from the test set!\n\n**test.csv** -- this data contains the test data for your model. You will feed this data into your regression model (for this challenge) and into the classification model. It is the same dataset for both, so you may use either challenge's data to generate your predictions.\n\n**sample_sub_reg.csv** -- An example of a correctly formatted submission for this challenge (with a random number provided as predictions for SalePrice. Please ensure that your submission to Kaggle matches this format.\n\n\n\nCodebook / Data Dictionary:\n\nhttps://raw.githubusercontent.com/storrgie/predict410/master/assignment/1/ameshouseing.txt",
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    {
      "name": "Description",
      "content": "Welcome to one of two Kaggle challenges for Project 3! As part of a successful submission for Project 3, we will expect you to make at least one (and hopefully, multiple!) submissions towards this classification challenge.\n\nIn this challenge, you will use the well known Ames housing data to create a regression model that predicts the sale price of a house in Ames, IA . You should feel free to use any and all features that are present in this dataset.\n\n**Goal**\n\nIt 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 variable. .\n\n\n**Evaluation**\n\nKaggle leaderboard standings will be determined by root mean squared error (RMSE).\n\nSubmission File Format\n\nThe file should contain a header and have the following format:.\n\n\n<pre>\nId,SalePrice\n1461,169000.1\n1462,187724.1233\n1463,175221\netc.\n</pre>",
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