{
  "fetched_at": "2026-10-19T23:34:14Z",
  "source_url": "https://www.kaggle.com/competitions/asrm-499552/data",
  "pages": [
    {
      "name": "data-description",
      "content": "<h2>File descriptions</h2>\n<ul>\n<li><strong>train.csv</strong> - the training data set</li>\n<li><strong>test.csv</strong> - the test data set</li>\n<li><strong>sampleSubmission.csv</strong> - a sample submission file in the correct format</li>\n</ul>\n\n<h2>Data fields</h2>\n<ul>\n<li><strong>Crash_Score </strong> - Measures the extent of the crash using factors such as number of injuries and fatalities, the number of vehicles involved, and other factors </li>\n<li><strong>Year </strong> - Calendar year of the crash </li>\n<li><strong>Month </strong> - Calendar month of the crash </li>\n<li><strong>Time_of_Day </strong> - Time of day, in four-hour blocks </li>\n<li><strong>Rd_Feature </strong> - Special feature of the road where the crash occurred </li>\n<li><strong>Rd_Character </strong> - Description of the road where the crash occurred </li>\n<li><strong>Rd_Class </strong> - Classification of the road type </li>\n<li><strong>Rd_Configuration </strong> - Design of the road </li>\n<li><strong>Rd_Surface </strong> - Material used for the road surface </li>\n<li><strong>Rd_Conditions </strong> - Condition of the road </li>\n<li><strong>Light </strong> - Lighting </li>\n<li><strong>Weather </strong> - Weather conditions </li>\n<li><strong>Traffic_Control </strong> - Any items that control traffic flow </li>\n<li><strong>Work_Area </strong> - Was the crash in a work area? </li>\n\n</ul>",
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    {
      "name": "Description",
      "content": "<p>Your actuarial consulting firm has been hired by the North Carolina Department of Transportation to help them understand the factors that contribute to the severity of vehicle crashes. For this preliminary study, they have obtained data from 2014-2019 on crashes in Cary, North Carolina (NC). If this investigation looks promising, they will provide statewide data for further analysis. </p>\n\n<p>The dataset used in this assessment was provided by the town of Cary, NC, and is used with permission. Some cleaning was performed in advance. The data dictionary at the end of this document describes the available variables. The target variable Crash_Score combines several factors, such as the number of injuries and fatalities and the number of vehicles involved. There is no scale to this variable other than larger values indicate a more severe crash. There is no information about the frequency of crashes. It is not necessary for you to paste the dictionary into your report. </p>\n\n<p>Your goal is to identify and interpret factors that relate to a higher or lower Crash_Score. </p>",
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    {
      "name": "Evaluation",
      "content": "<h2>Evaluation Metric</h2>\n<p>The evaluation metric for this competition is Root Mean Squared Error. RMSE, a standard way to measure the error of a model in predicting quantitative data, is defined as follows:</p>\n<p>$$R M S E=\\sqrt{\\sum_{i=1}^{n} \\frac{\\left(\\hat{y}_{i}-y_{i}\\right)^{2}}{n}},$$\n</p>\n<p>where $$\\hat{y}_{1}, \\hat{y}_{2}, \\ldots, \\hat{y}_{n}$$ are predicted values,$$ y_{1}, y_{2}, \\ldots, y_{n} $$ are observed values, and n is the number of observations.</p>\n\n<h2>Submission Format</h2>\n\n<p>Your submission file must have a header and should be structured in the following format:</p>\n<pre>id, predicted<br>1,1<br></pre>\n\n<p>An id column identifying the row and an expected column specifying the expected value of response variable predicted by your model.</p>",
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    {
      "name": "rules",
      "content": " - Don't cheat!\n - Apply yourself!\n - Have fun!",
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