{
  "fetched_at": "2026-10-20T00:22:50Z",
  "source_url": "https://www.kaggle.com/competitions/gdsc-outr-ml-event/data",
  "pages": [
    {
      "name": "data-description",
      "content": "This describes what files have been provided and the format of each.\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## Data Description:\n\n* ID: contains the id of the patient\n* PREFIX: Mr., Mrs. and so on, prefix of the name.\n* SUFFIX: Designation of the patient (e.g. PhD, MD, etc.)\n* MARITAL: Marital status, showing whether the patient is married or not\n* RACE: Race of the individual (black, white, asian, etc.)\n* ETHNICITY: Geographic location based ethnicity (hispanic, nohispanic, etc.)\n* GENDER: Gender of the individual (M or F)\n* BIRTHPLACE: Location of the birthplace\n* CITY: Name of the city where the patient belongs\n* STATE: State where the patient belongs to\n* COUNTY: County where the patient belongs to\n* ZIP: Zipcode of the individual's location\n* HEALTHCARE_EXPENSES: Expenses by the individual related to healthcare\n* HEALTHCARE_COVERAGE: Amount provided as healthcare coverage to the individual  \n\nThe target variable will be healthcare_coverage.",
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    {
      "name": "Description",
      "content": "Welcome to the healthcare coverage prediction challenge. We believe you guys had an awesome experience in the last three days due to the session. But no session is complete without something challenging at the end. It's time to apply your machine learning skills and predict the healthcare coverage for a group of patients based on certain data.\n\nHere is a handy link to Kaggle's [competition documentation](https://www.kaggle.com/docs/competitions), which includes, among other things, instructions on [submitting predictions](https://www.kaggle.com/docs/competitions#making-a-submission).\n\n\n\n",
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    {
      "name": "Evaluation",
      "content": "The evaluation metric for this competition is Root Mean Squared Error. The RMSE score is usually used in regression tasks to measure the error between the predicted and true value.\n\n### Submission Format\nThe file should contain a header and have the following format:\n```\nID,HEALTHCARE_COVERAGE\n1,1\n```",
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    {
      "name": "rules",
      "content": "The rules for the competition are simple:\n\n - Don't cheat!\n - Apply yourself!\n - Have fun!\n - Max size of team is 2",
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