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  "fetched_at": "2026-10-19T08:18:31Z",
  "source_url": "https://www.kaggle.com/competitions/eurecom-aml-2021-challenge-1/data",
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      "content": "## File Descriptions\n- **train_features.csv**: The training features (inputs).\n- **train_targets.csv**: The training targets (outputs).\n- **test_features.csv**: The test features (inputs).\n- **airports.csv**: Supplemental information about the airports.\n- **airlines.csv**: Supplemental information about the airlines.\n- **example_submission.csv**: An example submission file.\n\n\n## Data Fields\n\n**Features**\n\n- **ID**: Flight Identifier.\n- **YEAR**: Year of the Flight Trip.\n- **MONTH**: Month of the Flight Trip.\n- **DAY**: Day of the Flight Trip.\n- **DAY_OF_WEEK**: Day of week of the Flight Trip.\n- **AIRLINE**: Airline Identifier.\n- **FLIGHT_NUMBER**: Flight Identifier.\n- **TAIL_NUMBER**: Aircraft Identifier.\n- **ORIGIN_AIRPORT**: Starting Airport.\n- **DESTINATION_AIRPORT**: Destination Airport.\n- **SCHEDULED_DEPARTURE**: Planned Departure Time.\n- **DEPARTURE_TIME**: Actual Departure Time.\n- **DEPARTURE_DELAY**: Total Delay on Departure.\n- **TAXI_OUT**: The time duration elapsed between departure from the origin airport gate and wheels off.\n- **WHEELS_OFF**: The time point that the aircraft's wheels leave the ground.\n- **SCHEDULED_TIME**: Planned time amount needed for the flight trip.\n- **AIR_TIME**: The time duration between wheels_off and wheels_on time.\n- **DISTANCE**: Distance between two airports.\n- **SCHEDULED_ARRIVAL**: Planned arrival time.\n- **DIVERTED**: Aircraft landed on airport that out of schedule.\n- **CANCELLED**: Flight Cancelled (1 = cancelled).\n- **CANCELLATION_REASON**:  Reason for Cancellation of flight: A - Airline/Carrier; B - Weather; C - National Air System; D - Security.\n\n**Targets**\n\n- **ID**: Flight Identifier.\n- **ARRIVAL_DELAY**: The Observed Arrival Delay.\n\nTo have a better overview of the data, you can use Kaggle's Data Explorer below.\n",
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      "name": "Description",
      "content": "# Context\n\nEvery day, in US, there are thousands of flights departures and arrivals (prior to COVID): unfortunately, as you may have noticed yourself, flight delays are not a rare event!!\n\nIn this notebook, we play the role of a data scientist working in the travel industry, specifically on air transportation of passengers. We want to explore the data collected by the Department of Transportation (DoT) to understand passengers' behavior, as well as the properties of all flights, across several airline companies.\n\nNow, given historical data about flights in the country, including the delay information that was computed a-posteriori (so the ground truth is available), we want to build a model that can be used to predict how many minutes of delay a flight might experience in the future. This model should provide useful information for the airport to manage better its resources, to minimize the delays and their impact on the journey of its passengers. Alternatively, astute passengers could even use the model to choose the best time for flying, such as to avoid delays.",
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      "name": "Evaluation",
      "content": "## Goal\nThe goal of this challenge is to construct a model for predicting arrival delays.\n\n## Metric\nA leaderboard for this challenge will be ranked using the [Root Mean Squared Error (RMSE)](https://en.wikipedia.org/wiki/Root-mean-square_deviation) between the predicted values and the observed arrival delays. \n\n## Submission File Format\n\nYour submission is a CSV file containing your final model's predictions on the given test data. This file should contain a header and have the following format:\n    \n    ```\n    ID,ARRIVAL_DELAY\n    1561528,30.497905409756292\n    845015,32.75533601062071\n    717459,34.758212570279404\n    451496,32.73011833829676\n    etc.\n    ```\nYou can download an example submission file (sample_submission.csv) on the [Data page](https://www.kaggle.com/c/eurecom-aml-2021-challenge-1/data).",
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    {
      "name": "rules",
      "content": " - Don't cheat!\n - Apply yourself!\n - Have fun!",
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