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  "fetched_at": "2026-10-19T20:09:33Z",
  "source_url": "https://www.kaggle.com/competitions/formula-1-lap-time-prediction-nwvs-s00e02/data",
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    {
      "name": "data-description",
      "content": "The datasets provided are generated from Formula One races spanning 1996 through 2023. The goal is to predict the time required for drivers to complete laps on various circuits. Rows represent individual driver laps, and columns provide a set of descriptive features.\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*   `id` - unique identifier for the driver and lap\n*   `race` - name of the race\n*   `date` - date of the race (yyyy-mm-dd)\n*   `time` - time of the race (hh:mm:ss)\n*   `circuit` - name of the circuit\n*   `latitude` - latitude of the circuit\n*   `longitude` - longitude of the circuit\n*   `altitude` - altitude of the circuit\n*   `driver` - name of the driver\n*   `carNumber` - number of the car\n*   `constructor` - name of the constructor\n*   `avgDriverFinish` - average finishing position of the driver\n*   `avgConstructorFinish` - average finishing position of the constructor\n*   `lapNumber` - lap number of the race\n*   `lapPosition` - position of the driver at lap number\n*   `pitStop` - 1 = pit stop on the lap, 0 = no pit stop on the lap\n*   `pitCount` - cumulative count of driver's pit stops during race\n*   `pitTime_ms` - time of the pit stop\n*   `lapTime_ms` - time of the lap in milliseconds (TARGET)",
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    {
      "name": "Description",
      "content": "The datasets provided are generated from Formula One races spanning 1996 through 2023. The goal is to predict the time required for drivers to complete laps on various circuits. Rows represent individual driver laps, and columns provide a set of descriptive features.",
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    {
      "name": "Evaluation",
      "content": "The evaluation metric for this competition is [RMSE](https://en.wikipedia.org/wiki/Root-mean-square_deviation).\n\nThe public leaderboard is calculated with only 1% of the test set (207 laps) and may be volatile.\nThe private leaderboard will rank submissions on the remaining 99% of the test set.\n\n## Submission Format\n\nFor every driver lap in the test.csv dataset, submission files should contain two columns: `id` and `lapTime_ms`. \n\nThe file should contain a header and have the following format:\n```\nid,lapTime_ms\n30001,94205\n30002,74854\n30003,89379\netc.\n```",
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    {
      "name": "rules",
      "content": " - Don't cheat!\n - Apply yourself!\n - Have fun!",
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    {
      "name": "abstract",
      "content": "## In this episode of Nick Wan Versus (NWVS),\n\nNick takes on all challengers in a live data science competition to predict the time required for Formula One drivers to complete laps on various circuits. This competition features a new dataset and just two hours to code a solution to the prediction challenge. The evaluation metric for submissions is RMSE.\n\n**Episode 2:** Live on Twitch Tuesday January 16 at 9PM ET: https://www.twitch.tv/nickwan_datasci. Competition ends at 11PM ET.",
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