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  "fetched_at": "2026-10-20T10:28:37Z",
  "source_url": "https://www.kaggle.com/competitions/anomaly-dectection-for-financial/data",
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    {
      "name": "data-description",
      "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*   **metaData.csv** - supplemental information about the data\n\n## Columns\n\n*   `Anomaly_label` - the target label\n*   `Vision` - the features that represent the increase (0) or decrease (1) of VDI compared to the previous quarter.",
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      "name": "Description",
      "content": "Welcome to DS108.O21 Assignment 6 competition. In this competition, you are given a VDI Process dataset that was binary encoded. From this dataset, you will write code to clean and predict the status of samples of all time in the testing set.\n\nWhen you submit a prediction, a 40% subset of it will be evaluated against the true labels and you get a \"public score\" for some feedback. The final score will be evaluated on the other 60%.\n\nGive it your best shot and have fun!😁 😁 😁",
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      "name": "Evaluation",
      "content": "The evaluation metric for this competition is [Macro F1-Score](https://en.wikipedia.org/wiki/F-score). The F1 score, commonly used in information retrieval, measures accuracy using the statistics precision \\( \\text{p} \\) and recall \\( \\text{r} \\).\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.",
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    {
      "name": "rules",
      "content": "\n\n - Don't cheat!\n - Apply yourself!\n - Have fun!",
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