✈️ Flight Delay Prediction 🕒

🇺🇸 Context

The Federal Aviation Administration wants to develop a system capable of predicting flight arrival delays (in minutes) based on historical information about airlines, airports, and operational conditions.

For this purpose, you are provided with a dataset aggregated at the level of:
(year, month, airline, airport).

📦 Available Features

Name	Description
sample_id	Unique sample ID (e.g., 0194048)
year	Reporting year
month	Reporting month
carrier	Airline code
carrier_name	Full airline name
airport	Airport code (IATA)
airport_name	Airport name
arr_flights	Total number of arrived flights
arr_del15	Number of arrivals delayed >15 minutes
carrier_ct	Delays attributed to the carrier
weather_ct	Delays caused by weather conditions
nas_ct	Delays caused by the national air system (NAS)
security_ct	Security-related delays
late_aircraft_ct	Delays caused by late-arriving aircraft
arr_cancelled	Cancelled flights
arr_diverted	Diverted flights
delay	Target variable — total arrival delay (minutes, available only in train.csv)

Note: In test.csv, the delay column is absent and needs to be predicted.

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🎯 Problem Objective

Train a model to predict delay (in minutes) using the features listed above.

The final result should be submitted as a submission.csv file.

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📝 Submission Format

The file should be:

sample_id,delay
0194048,132
0194049,0
0194050,215

where:

• sample_id must match the values in test.csv
• delay represents an integer, the model's predicted delay

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📊 Evaluation

Evaluation is performed using MAE (Mean Absolute Error):

🏆 Final Score

• MAE ≤ 400 → 100 points
• MAE ≥ 600 → 0 points
• Intermediate values receive proportional scores between 0 and 100.

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🔎 Notes

• You are free to use any ML technique.
• Preprocessing, encoding, and feature engineering are allowed.

Good luck and smooth flying! ✨