Baselight

Predict Commercial Flight Delays [50+ Features]

Data on Every Commercial Flight US - January 2024 (including delays and reasons)

@kaggle.oleksiimartusiuk_bts_january_2024_commercial_flights_data

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About this Dataset

Predict Commercial Flight Delays [50+ Features]

This dataset provides a comprehensive record of commercial flights in the United States during January 2024. It offers valuable insights for researchers, data analysts, and anyone interested in understanding air travel patterns within the US.

Key Features:

  • Flight Details: Includes information such as origin and destination airports, flight numbers, airlines, scheduled and actual departure/arrival times, and aircraft types.
  • Scheduling Data: Provides insights into flight schedules, including planned departure and arrival times, potential delays, and cancellations. This allows analysis of on-time performance and potential factors impacting schedules.

Potential Use Cases:

  • Air Travel Research: Analyze trends in flight routes, airlines, and aircraft utilization.
  • Travel Demand Analysis: Understand passenger volume patterns across different routes and days of the week.
  • Flight Performance Evaluation: Assess on-time performance, delays, and cancellations for different airlines and routes.
  • Predictive Modeling: Develop models to predict future flight delays, cancellations, and passenger demand.

Tables

T Ontime Marketing

@kaggle.oleksiimartusiuk_bts_january_2024_commercial_flights_data.t_ontime_marketing
  • 14.25 MB
  • 582425 rows
  • 59 columns
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CREATE TABLE t_ontime_marketing (
  "fl_date" VARCHAR,
  "mkt_unique_carrier" VARCHAR,
  "op_unique_carrier" VARCHAR,
  "tail_num" VARCHAR,
  "op_carrier_fl_num" BIGINT,
  "origin_city_market_id" BIGINT,
  "origin_city_name" VARCHAR,
  "origin_state_abr" VARCHAR,
  "dest_city_market_id" BIGINT,
  "dest_city_name" VARCHAR,
  "dest_state_abr" VARCHAR,
  "crs_dep_time" BIGINT,
  "dep_time" DOUBLE,
  "dep_delay" DOUBLE,
  "taxi_out" DOUBLE,
  "wheels_off" DOUBLE,
  "wheels_on" DOUBLE,
  "taxi_in" DOUBLE,
  "arr_time" DOUBLE,
  "arr_delay" DOUBLE,
  "arr_delay_new" DOUBLE,
  "cancelled" DOUBLE,
  "cancellation_code" VARCHAR,
  "diverted" DOUBLE,
  "dup" VARCHAR,
  "distance" DOUBLE,
  "carrier_delay" DOUBLE,
  "weather_delay" DOUBLE,
  "nas_delay" DOUBLE,
  "security_delay" DOUBLE,
  "late_aircraft_delay" DOUBLE,
  "first_dep_time" DOUBLE,
  "total_add_gtime" DOUBLE,
  "div_airport_landings" BIGINT,
  "div_actual_elapsed_time" DOUBLE,
  "div1_airport" VARCHAR,
  "div1_wheels_on" DOUBLE,
  "div1_total_gtime" DOUBLE,
  "div1_wheels_off" DOUBLE,
  "div1_tail_num" VARCHAR,
  "div2_airport" VARCHAR,
  "div2_wheels_on" DOUBLE,
  "div2_total_gtime" DOUBLE,
  "div2_wheels_off" DOUBLE,
  "div2_tail_num" VARCHAR,
  "div3_airport" VARCHAR,
  "div3_wheels_on" DOUBLE,
  "div3_total_gtime" DOUBLE,
  "div3_wheels_off" DOUBLE,
  "div3_tail_num" VARCHAR,
  "div4_airport" VARCHAR,
  "div4_wheels_on" VARCHAR,
  "div4_total_gtime" VARCHAR,
  "div4_wheels_off" VARCHAR,
  "div4_tail_num" VARCHAR,
  "div5_airport" VARCHAR,
  "div5_wheels_on" VARCHAR,
  "div5_wheels_off" VARCHAR,
  "div5_tail_num" VARCHAR
);

Consumer Airfare Report Table 2 Top 1–000 City Pair Ma 7b32a5be

@kaggle.oleksiimartusiuk_bts_january_2024_commercial_flights_data.consumer_airfare_report_table_2_top_1_000_city_pair_ma_7b32a5be
  • 392.8 KB
  • 8027 rows
  • 16 columns
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CREATE TABLE consumer_airfare_report_table_2_top_1_000_city_pair_ma_7b32a5be (
  "tbl" VARCHAR,
  "year" BIGINT,
  "quarter" BIGINT,
  "citymarketid" BIGINT,
  "city" VARCHAR,
  "markets" BIGINT,
  "cur_passengers" BIGINT,
  "cur_fare" DOUBLE,
  "cur_yield" DOUBLE,
  "distance" DOUBLE,
  "ly_passengers" DOUBLE,
  "ly_fare" DOUBLE,
  "ly_yield" DOUBLE,
  "ly_distance" DOUBLE,
  "geocoded_city" VARCHAR,
  "tbl2pk" BIGINT
);

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