{
  "fetched_at": "2026-10-20T05:12:07Z",
  "source_url": "https://www.kaggle.com/competitions/datathon-21/data",
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      "content": "## 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\n\n* Target_Death_Rate: Dependent variable. Mean per capita (100,000) cancer mortalities(a)\n\n* RateOfIncidence: Mean per capita (100,000) cancer diagoses(a)\n\n* medianIncome: Median income per county (b)\n\n* Income: Percent of populace in poverty (b)\n\n* Poverty%: Percent of populace in poverty\n\n* Age: Median age of county residents (b)\n\n* Ages_men: Median age of male county residents (b)\n\n* Ages_women: Median age of female county residents (b)\n\n* Location: County name (b)\n\n* SizeOfHousehold: Mean household size of county (b)\n\n* Married%: Percent of county residents who are married (b)\n\n* Edu_less_HS: Percent of county residents ages 18-24 highest education attained: less than high school (b)\n\n* Edu_HSD: Percent of county residents ages 18-24 highest education attained: high school diploma (b)\n\n* Edu_Clg: Percent of county residents ages 18-24 highest education attained: some college (b)\n\n* Edu_Bachdegree: Percent of county residents ages 18-24 highest education attained: bachelor's degree (b)\n\n* Edu_HSD25: Percent of county residents ages 25 and over highest education attained: high school diploma (b)\n\n* Edu_Bachdegree25: Percent of county residents ages 25 and over highest education attained: bachelor's degree (b)\n\n* 16+_Employed: Percent of county residents ages 16 and over employed (b)\n\n* 16+_Unemployed: Percent of county residents ages 16 and over unemployed (b)\n\n* Prv_Public_HealthCov: Percent of county residents with private health coverage (b)\n\n* Prv_HealthCov: Percent of county residents with private health coverage alone (no public assistance) (b)\n\n* Employ_PrvCov: Percent of county residents with employee-provided private health coverage (b)\n\n* Gov_HealthCov: Percent of county residents with government-provided health coverage (b)\n\n* Gov_HealthCov_Alone: Percent of county residents with government-provided health coverage alone (b)\n\n* White_Residents: Percent of county residents who identify as White (b)\n\n* Black_Residents: Percent of county residents who identify as Black (b)\n\n* Married: Percent of married households (b)\n\n* Rate_Of_Birth: Number of live births relative to number of women in county (b)\n\n(a): years 2010-2016\n\n(b): 2013 Census Estimates",
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    {
      "name": "Description",
      "content": "Welcome to \"Datathon2021\"! Gear yourselves up for an exciting real-world application challenge that we have in store for you. This is where you will have to give your brain a workout and use your skills, to make a Regression Model for the given situation!\n\n*About the Data*\nThis dataset includes various attributes of the average deaths caused by cancer, mean number of reported cases of cancer diagnosed annually, etc in in various countries. The dataset comprises of over 3000 instances which need to be analyzed and used to predict the cancer mortality rates in the US countries. \n\n*Task*\nYour task is to build a regression model to predict the target death rate.\nGood Luck and Have Fun!",
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    {
      "name": "Evaluation",
      "content": "Coefficient of determination also called as R2 score is used to evaluate the performance of a linear regression model. It is the amount of the variation in the output dependent attribute which is predictable from the input independent variable(s). It is used to check how well-observed results are reproduced by the model, depending on the ratio of total deviation of results described by the model.",
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    {
      "name": "rules",
      "content": "This is a page where you can include rules that participants must accept before joining. You may wish to include rules like:\n\n - Don't cheat!\n - Apply yourself!\n ",
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