{
  "fetched_at": "2026-10-20T03:35:31Z",
  "source_url": "https://www.kaggle.com/competitions/become-a-kaggle-master-hw1/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, originally prepared by Dean De Cock but lightly edited to match the column names used here\n- sample_submission.csv - a benchmark submission from a linear regression on year and month of sale, lot square footage, and number of bedrooms\n\n# Data fields\nYou can find a description of all 79 features in features_description.csv",
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    {
      "name": "Description",
      "content": "# Context\nThis is homework 1 of the course 'Become a Kaggle Master'.\nThis course is part of the <a href=\"https://dauphine.psl.eu/en/training/masters-degrees/computer-science/m2-artificial-intelligence-systems-data\">IASD Master 2 in Machine Learning, Artificial Intelligence, and Data Science</a> program, jointly run by <a href=\"https://dauphine.psl.eu/en/\">Université Paris Dauphine</a>, <a href=\"https://www.ens.psl.eu/en\"> ENS</a>  and <a href=\"https://mines-paristech.eu/\"> Mines ParisTech</a>.\n\n# Goal\nThe goal of the competition is to predict house prices using a modified version of the Ames Housing dataset, which includes 79 features describing various aspects of residential homes. The objective is to develop a model that can accurately predict house prices based on the provided features. The competition is open to all students enrolled in the course and serves as an introduction to Kaggle and practical machine learning",
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    {
      "name": "Evaluation",
      "content": "# Goal\nThis competition serves as an opportunity to apply and practice advanced regression techniques. It is suggested to begin with Exploratory Data Analysis to gain insight into the data and then to use various regularized regression techniques and sophisticated machine learning models to make predictions.\n\n#Metric\nSubmissions are evaluated on the <a href=\"https://en.wikipedia.org/wiki/Root_mean_square\">\nRoot-Mean-Squared-Error </a> of the predicted value and the the actual sales price. \n\n# Submission File Format\nThe file should contain a header and have the following format:\nId,Predicted\n1461,169000.1\n1462,187724.1233\n1463,175221\netc.\n\n\n\n",
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    },
    {
      "name": "rules",
      "content": "# No external data allowed in this competition\nIf participants are using external data, they will be automatically disqualified.\n\n\nTwo participants per group\nYou cannot sign up to Kaggle from multiple accounts and therefore you cannot submit from multiple accounts.\n\nNo private sharing outside teams\nPrivately sharing code or data outside of teams is not permitted. It's okay to share code if made available to all participants on the forums.\n\nTeam Limits\nThe maximum team size is 2",
      "isPublished": false,
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    }
  ]
}