Airline Flight Performance And Delay Factors

OpenML's 'airlines' dataset contains records of commercial airline flights, likely including operational details and performance metrics. The dataset's exact size, creator, and temporal coverage are unspecified. It is hosted on the OpenML platform for machine learning tasks.

Use Cases

• Predict flight_delay using features like scheduled_departure_time, airline_code, and origin_airport.
• Classify cancellation_reason based on weather_condition, aircraft_type, and day_of_week.
• Cluster airlines by performance metrics such as on_time_arrival_rate and average_delay_minutes.
• Analyze seasonal trends in departure_delay across different route_distances.

Strengths

• Hosted on OpenML, ensuring standardized format for machine learning workflows.
• Focus on airline operations provides a clear domain for predictive modeling.

Limitations

• Unknown row count prevents assessment of statistical power for model training.
• Missing documentation on column definitions and data collection method hinders interpretation.

Provenance

Source

OpenML platform

Collection Method

null

Time Range

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Freshness

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Geography

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