{
  "fetched_at": "2026-10-20T03:12:37Z",
  "source_url": "https://www.kaggle.com/competitions/final-project-predictive-analytics/data",
  "pages": [
    {
      "name": "data-description",
      "content": "\nThis dataset contains information about used cars.\nBased on training data, you are required to file build regression models to predict the price of cars.\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## Columns\n\n*   `example_id` - definition of example_id\n*   `feature_1` - definition of feature_1\n*   etc.",
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      "hasIsPublished": false,
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    },
    {
      "name": "Description",
      "content": "This dataset contains information about used cars.\nBased on training data, you are required to file build regression models to predict the price of cars.\n\n## Acknowledgements\n\nWe thank Mr. Nguyen Thanh Tuan for providing this dataset.",
      "isPublished": false,
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      "postTitle": "",
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    {
      "name": "Evaluation",
      "content": "The evaluation pages describes how submissions will be scored and how students should format their submissions. You don't need a sub-title at the top; the page title appears above. For an example of a high quality evaluation page, see the 2013 KDD Cup page, which we have included below:\n\n---\n\nThe evaluation metric for this competition is [Root mean square error]. The root mean square error, commonly used to measure the accuracy in regression problem:\n\n\\[ RMSE = \\sqrt{ \\cfrac{\\sum\\limits_{i=1}^n \\ (y_i - \\hat{y}_i)^2}{n} } \\]\n\nwhere \\[n\\] is the number of observations in the test data set; \\[y_i\\] is the \\[i^{th}\\] true value and \\[\\hat{y}_i\\] is the \\[i^{th}\\] predicted value\n\nPrecision is the ratio of true positives \\(\\text{tp}\\) to all predicted positives \\(\\text{tp} + \\text{fp}\\). Recall is the ratio of true positives \\(\\text{tp}\\) to all actual positives \\(\\text{tp} + \\text{fn}\\). The F1 score is given by:\n\n\\[ \\text{F1} = 2\\frac{\\text{p} \\cdot \\text{r}}{\\text{p}+\\text{r}}\\ \\ \\mathrm{where}\\ \\ \\text{p} = \\frac{\\text{tp}}{\\text{tp}+\\text{fp}},\\ \\ \\text{r} = \\frac{\\text{tp}}{\\text{tp}+\\text{fn}} \\]\n\nThe F1 metric weights recall and precision equally, and a good retrieval algorithm will maximize both precision and recall simultaneously. Thus, moderately good performance on both will be favored over extremely good performance on one and poor performance on the other.\n\n## Submission Format\n\n**For every author in the dataset**, submission files should contain two columns: `AuthorId` and `DuplicateAuthorIds`. `DuplicateAuthorIds` should be a space-delimited list. Every `AuthorId` counts as its own duplicate, and every duplicate should be listed under each of its respective ids. For example, if you suspect author A, B, and C are the same, you should list `A,A B C`, `B,B A C`, `C,C A B`.\n\n\nThe file should contain a header and have the following format:\n```\nAuthorId,DuplicateAuthorIds\n1,1\n```",
      "isPublished": false,
      "hasIsPublished": false,
      "postTitle": "",
      "hasPostTitle": false,
      "mimeType": "",
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    },
    {
      "name": "rules",
      "content": "Who has the data has the power!\n\n ",
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}