The number of flights performed globally by the airline industry has increased steadily since the early 2000s and reached **** million in 2019. However, due to the coronavirus pandemic, the number of flights dropped to **** million in 2020. The flight volume increased again in the following years and was forecasted to reach ** million in 2025.

This record is a global open-source passenger air traffic dataset primarily dedicated to the research community. It gives a seating capacity available on each origin-destination route for a given year, 2019, and the associated aircraft and airline when this information is available. Context on the original work is given in the related article (https://journals.open.tudelft.nl/joas/article/download/7201/5683) and on the associated GitHub page (https://github.com/AeroMAPS/AeroSCOPE/).A simple data exploration interface will be available at www.aeromaps.eu/aeroscope.The dataset was created by aggregating various available open-source databases with limited geographical coverage. It was then completed using a route database created by parsing Wikipedia and Wikidata, on which the traffic volume was estimated using a machine learning algorithm (XGBoost) trained using traffic and socio-economical data. 1- DISCLAIMER The dataset was gathered to allow highly aggregated analyses of the air traffic, at the continental or country levels. At the route level, the accuracy is limited as mentioned in the associated article and improper usage could lead to erroneous analyses. Although all sources used are open to everyone, the Eurocontrol database is only freely available to academic researchers. It is used in this dataset in a very aggregated way and under several levels of abstraction. As a result, it is not distributed in its original format as specified in the contract of use. As a general rule, we decline any responsibility for any use that is contrary to the terms and conditions of the various sources that are used. In case of commercial use of the database, please contact us in advance. 2- DESCRIPTION Each data entry represents an (Origin-Destination-Operator-Aircraft type) tuple. Please refer to the support article for more details (see above). The dataset contains the following columns:

"First column" : index airline_iata : IATA code of the operator in nominal cases. An ICAO -> IATA code conversion was performed for some sources, and the ICAO code was kept if no match was found. acft_icao : ICAO code of the aircraft type acft_class : Aircraft class identifier, own classification.

WB: Wide Body NB: Narrow Body RJ: Regional Jet PJ: Private Jet TP: Turbo Propeller PP: Piston Propeller HE: Helicopter OTHER seymour_proxy: Aircraft code for Seymour Surrogate (https://doi.org/10.1016/j.trd.2020.102528), own classification to derive proxy aircraft when nominal aircraft type unavailable in the aircraft performance model. source: Original data source for the record, before compilation and enrichment.

ANAC: Brasilian Civil Aviation Authorities AUS Stats: Australian Civil Aviation Authorities BTS: US Bureau of Transportation Statistics T100 Estimation: Own model, estimation on Wikipedia-parsed route database Eurocontrol: Aggregation and enrichment of R&D database OpenSky World Bank seats: Number of seats available for the data entry, AFTER airport residual scaling n_flights: Number of flights of the data entry, when available iata_departure, iata_arrival : IATA code of the origin and destination airports. Some BTS inhouse identifiers could remain but it is marginal. departure_lon, departure_lat, arrival_lon, arrival_lat : Origin and destination coordinates, could be NaN if the IATA identifier is erroneous departure_country, arrival_country: Origin and destination country ISO2 code. WARNING: disable NA (Namibia) as default NaN at import departure_continent, arrival_continent: Origin and destination continent code. WARNING: disable NA (North America) as default NaN at import seats_no_est_scaling: Number of seats available for the data entry, BEFORE airport residual scaling distance_km: Flight distance (km) ask: Available Seat Kilometres rpk: Revenue Passenger Kilometres (simple calculation from ASK using IATA average load factor) fuel_burn_seymour: Fuel burn per flight (kg) when seymour proxy available fuel_burn: Total fuel burn of the data entry (kg) co2: Total CO2 emissions of the data entry (kg) domestic: Domestic/international boolean (Domestic=1, International=0)

3- Citation Please cite the support paper instead of the dataset itself.

Salgas, A., Sun, J., Delbecq, S., Planès, T., & Lafforgue, G. (2023). Compilation of an open-source traffic and CO2 emissions dataset for commercial aviation. Journal of Open Aviation Science. https://doi.org/10.59490/joas.2023.7201

1) Data Introduction • The European Flights Dataset is a tabulated dataset of more than 680,000 air traffic records, including instrument flight (IFR) arrivals and operations at major European airports from January 2016 to May 2022.

2) Data Utilization (1) European Flights Dataset has characteristics that: • Each row contains 14 key items, including year, month, flight date, airport code and name, country name, and number of departures, arrivals, and total flights based on IFR. • The data are segmented by airport, country, and month, so they are well structured to analyze time series and spatial changes in European air traffic. (2) European Flights Dataset can be used to: • Analysis of Air Traffic Trends and Recovery: Using IFR operational performance by year, month, and airport, you can analyze changes in air traffic before and after the pandemic, seasonal trends, and speed of recovery. • Airport and Country Comparison Study: National/Airport performance data can be used to compare and evaluate major hub airports, cross-country aviation network structure, policy effectiveness, and more.

In 2023, the estimated number of scheduled passengers boarded by the global airline industry amounted to approximately *** billion people. This represents a significant increase compared to the previous year since the pandemic started and the positive trend was forecast to continue in 2024, with the scheduled passenger volume reaching just below **** billion travelers. Airline passenger traffic The number of scheduled passengers handled by the global airline industry has increased in all but one of the last decade. Scheduled passengers refer to the number of passengers who have booked a flight with a commercial airline. Excluded are passengers on charter flights, whereby an entire plane is booked by a private group. In 2023, the Asia Pacific region had the highest share of airline passenger traffic, accounting for ********* of the global total.

Dataset Overview: Flight Price Dataset of Bangladesh

Introduction

The "Bangladesh Flight Fare Dataset" is a synthetic dataset comprising 57,000 flight records tailored to represent air travel scenarios originating from Bangladesh. This dataset simulates realistic flight fare dynamics, capturing key factors such as airline operations, airport specifics, travel classes, booking behaviors, and seasonal variations specific to Bangladesh’s aviation market. It is designed for researchers, data scientists, and analysts interested in flight fare prediction, travel pattern analysis, or machine learning/deep learning applications. By combining real-world inspired statistical distributions and aviation industry standards, this dataset provides a robust foundation for exploring flight economics in a South Asian context.

Dataset Purpose

This dataset aims to: - Facilitate predictive modeling of flight fares, with "Total Fare (BDT)" as the primary target variable. - Enable analysis of travel trends, including the impact of cultural festivals (e.g., Eid, Hajj) and booking timings on pricing. - Serve as a training resource for machine learning (ML) and deep learning (DL) models, with sufficient sample size (50,000) and feature diversity for generalization. - Provide a realistic yet synthetic representation of Bangladesh’s air travel ecosystem, blending domestic and international flight scenarios.

Data Collection and Methodology

The dataset is synthetically generated using Python, with its methodology rooted in real-world aviation data and statistical principles. Below is a detailed breakdown of its construction:

1. Data Components

• Airlines:
  • Count: 25 airlines (21 international, 4 domestic).
  • Source: Compiled from Bangladesh Civil Aviation Authority and Airline History, including major carriers like Emirates, Qatar Airways, and Biman Bangladesh Airlines.
  • Selection: Random uniform choice per flight record to reflect operational diversity.
• Airports:
  • Source Airports: 8 domestic airports (e.g., DAC - Hazrat Shahjalal International Airport, Dhaka).
  • Destination Airports: 20 airports (8 domestic + 12 international, e.g., DXB - Dubai International Airport).
  • Coordinates: Sourced from World Airport Codes, used for distance calculations.
  • Full Names: Added for readability, mapped via a dictionary (e.g., "DAC" → "Hazrat Shahjalal International Airport, Dhaka").
• Travel Classes: Economy, Business, First Class, standard across the industry, randomly assigned with uniform distribution.
• Booking Sources: Direct Booking, Travel Agency, Online Website, reflecting common methods, per Statista, with uniform random selection.
• Aircraft Types: Boeing 777, Airbus A320, Boeing 737, Boeing 787, Airbus A350, assigned based on flight distance, sourced from Boeing and Airbus.

2. Key Calculations

• Distance:

  • Method: Haversine formula calculates great-circle distance: a = sin²(Δφ/2) + cos(φ₁) cos(φ₂) sin²(Δλ/2) c = 2 arctan2(√a, √(1-a)) d = R · c, R = 6371 km

• Purpose: Determines flight duration, aircraft type, and stopovers.

• Source: Wikipedia - Haversine Formula.

• Flight Duration:

• Formula: Duration = max(d/s · U(0.9, 1.1), 0.5), where s is speed (300 km/h for <500 km, 600 km/h for 500-2000 km, 900 km/h for >2000 km), and U is uniform random variation.

• Source: Speeds adjusted from World Atlas, ensuring realism (e.g., DAC to CGP ~45 minutes).

• Fares:

• Base Fares:

• Domestic: Economy (2000-5000 BDT), Business (5000-10000 BDT), First Class (10000-15000 BDT).

• International: Economy (5000-70000 BDT), Business (15000-150000 BDT), First Class (25000-300000 BDT).

• Source: Derived from Trip.com and Expedia, e.g., DAC to LHR ~$380-600 (~41800-66000 BDT at 1 USD = 110 BDT).

• Adjustments:

• Seasonal multipliers (Regular: 1.0, Eid: 1.3, Hajj: 1.5, Winter: 1.2), per demand trends from Timeanddate.com.

• Days Before Departure: 20% discount (60+ days), 10% discount (30-59 days), 20% surge (<5 days), per Skyscanner.

• Taxes: Domestic: 200 BDT; International: 2000-6000 BDT + 15% base fare, per [Bangladesh Civil Aviation Authority](https://www.dgca.g...

Daily data showing UK flight numbers and rolling seven-day average, including flights to, from, and within the UK. These are official statistics in development. Source: EUROCONTROL.

This dataset contains (i) a set of worldwide airports that are relevant for the global air cargo transport (ii) a dataset containing aircraft-specific yearly recorded frequencies (referring to the year 2014) for all passenger and cargo airlines (integrators such as FedEx are excluded) for all different origin destination (OD) airport pairs (iii) a dataset for each integrator FedEx, UPS, DHL with yearly estimated cargo capacity (expressed in tonnes) referring to the year 2019 for every OD airport pair. The estimation was based on a dataset containing all recorded flights for each OD airport pair of interest, which was filtered to extrapolate only flights operated by the integrators

Monthly number of passengers carried (arrivals plus departures), by type of transport (national, international, intra- and extra-EU). Passengers carried are (1) all passengers on a particular flight (with one flight number) counted once only and not repeatedly on each individual stage of that flight, (2) all revenue and non-revenue passengers whose journey begins or terminates at the reporting airport and transfer passengers joining or leaving the flight at the reporting airport. Excludes direct transit passengers. National aggregates, total intra-EU aggregates and total EU aggregates exclude any double counting.

This layer visualizes over 60,000 commercial flight paths. The data was obtained from openflights.org, and was last updated in June 2014. The site states, "The third-party that OpenFlights uses for route data ceased providing updates in June 2014. The current data is of historical value only. As of June 2014, the OpenFlights/Airline Route Mapper Route Database contains 67,663 routes between 3,321 airports on 548 airlines spanning the globe. Creating and maintaining this database has required and continues to require an immense amount of work. We need your support to keep this database up-to-date."To donate, visit the site and click the PayPal link.Routes were created using the XY-to-line tool in ArcGIS Pro, inspired by Kenneth Field's work, and following a modified methodology from Michael Markieta (www.spatialanalysis.ca/2011/global-connectivity-mapping-out-flight-routes).Some cleanup was required in the original data, including adding missing location data for several airports and some missing IATA codes. Before performing the point to line conversion, the key to preserving attributes in the original data is a combination of the INDEX and MATCH functions in Microsoft Excel. Example function: =INDEX(Airlines!$B$2:$B$6200,MATCH(Routes!$A2,Airlines!$D$2:Airlines!$D$6200,0))                                                

United States No. of Flights: SEA Terminal: South Satellite data was reported at 38.000 Unit in 16 May 2025. This records a decrease from the previous number of 43.000 Unit for 15 May 2025. United States No. of Flights: SEA Terminal: South Satellite data is updated daily, averaging 31.000 Unit from May 2008 (Median) to 16 May 2025, with 6219 observations. The data reached an all-time high of 48.000 Unit in 19 Apr 2025 and a record low of 0.000 Unit in 08 May 2018. United States No. of Flights: SEA Terminal: South Satellite data remains active status in CEIC and is reported by U.S. Customs and Border Protection. The data is categorized under Global Database’s United States – Table US.TA: Airport Statistics: Number of Flights: by Airport. [COVID-19-IMPACT]

For a more comprehensive dataset with many more features check out the "Yacht/Motorboat Pricing Data (10,000+ listings)" dataset.

Link below: https://www.kaggle.com/artemkorottchenko/large-boatyacht-pricing-dataset

Context

What are the most important features in determining the price of a new or used aircraft? Is it the aircraft type? Year? Manufacturer? Other characteristics?

This is one of many questions regarding the used/new aircraft markets I hope to answer with this dataset.

The dataset contains over 2000 aircraft that are for sale around the world. The data was scraped during July of 2020.

Content

The data was scraped from various websites using the Scrapy framework for Python.

Scrapy script:

https://github.com/akorott/Aircraft-Scrapy-Script.git

Content scraped: - New/Used - Price
- Currency (USD, EUR, GBP) - Category - Year - Make - Model - Location - Serial number - Registration number - Total hours - Engine 1 hours - Engine 2 hours - Prop 1 hours - Prop 2 hours - Total Seats - Flight Rules - National Origin

Keep in mind that the data was scraped from 2 different sources. Some of the data (New/Used, Engine 1 hours, Engine 2 hours, Prop 1 hours, Prop 2 hours, Total Seats, Flight Rules) was only easily accessible on one source, thus is missing for part of the dataset.

FAQ

Flight Rules: Visual Flight Rules (VFR) VS Instrument Flight Rules (IFR). In a nutshell, an aircraft equipped with IFR is one where a pilot can fully navigate an aircraft using instruments in the cockpit. Any aircraft flying over 18,000 feet, by law, has to be equipped with IFR equipment.

BTH - Beyond the Horizon - according to my research, BTH means that an aircraft is equipped with a radar, but doesn't fully meet IFR criteria.

VFR - (https://en.wikipedia.org/wiki/Visual_flight_rules) IFR - (https://en.wikipedia.org/wiki/Instrument_flight_rules)

Some of the acronyms used within total hours, engine 1, engine 2, prop 1, prop 2 columns: SMOH - Since major overhaul SNEW - Since new SPOH - Since prop overhaul SFOH - Since factory overhaul (more reliable) SOH - Since overhaul STOH - Since top overhaul SFRM - Since factory re-manufactured

Thank you for checking out this dataset and happy kaggling!

Turkish Airlines: AS: International Flights: Europe data was reported at 45,369,748.000 Unit/km th in 2017. This records a decrease from the previous number of 47,590,992.000 Unit/km th for 2016. Turkish Airlines: AS: International Flights: Europe data is updated yearly, averaging 36,721,737.000 Unit/km th from Dec 2009 (Median) to 2017, with 9 observations. The data reached an all-time high of 47,590,992.000 Unit/km th in 2016 and a record low of 20,646,524.000 Unit/km th in 2009. Turkish Airlines: AS: International Flights: Europe data remains active status in CEIC and is reported by Turkish Airlines, Incorporation. The data is categorized under Global Database’s Turkey – Table TR.TA011: Airlines Statistics: Turkish Airlines .

India All Scheduled Airlines: Domestic: Number of Flight data was reported at 102,319.000 Unit in Mar 2025. This records an increase from the previous number of 92,291.000 Unit for Feb 2025. India All Scheduled Airlines: Domestic: Number of Flight data is updated monthly, averaging 48,100.000 Unit from Apr 2001 (Median) to Mar 2025, with 288 observations. The data reached an all-time high of 102,319.000 Unit in Mar 2025 and a record low of 188.000 Unit in Apr 2020. India All Scheduled Airlines: Domestic: Number of Flight data remains active status in CEIC and is reported by Directorate General of Civil Aviation. The data is categorized under India Premium Database’s Transportation, Post and Telecom Sector – Table IN.TA019: Airline Statistics: All Scheduled Airlines.

United States No. of Flights: JFK Terminal: 1 data was reported at 35.000 Unit in 16 May 2025. This records a decrease from the previous number of 36.000 Unit for 15 May 2025. United States No. of Flights: JFK Terminal: 1 data is updated daily, averaging 0.000 Unit from May 2008 (Median) to 16 May 2025, with 6215 observations. The data reached an all-time high of 0.000 Unit in 16 May 2025 and a record low of 0.000 Unit in 16 May 2025. United States No. of Flights: JFK Terminal: 1 data remains active status in CEIC and is reported by U.S. Customs and Border Protection. The data is categorized under Global Database’s United States – Table US.TA: Airport Statistics: Number of Flights: by Airport. [COVID-19-IMPACT]

The environmental impact of aviation is enormous given the fact that in the US alone there are nearly 6 million flights per year of commercial aircraft. This situation has driven numerous policy and procedural measures to help develop environmentally friendly technologies which are safe and affordable and reduce the environmental impact of aviation. However, many of these technologies require significant initial investment in newer aircraft fleets and modifications to existing regulations which are both long and costly enterprises. We propose to use an anomaly detection method based on Virtual Sensors to help detect overconsumption of fuel in aircraft which relies only on the data recorded during flight of most existing commercial aircraft, thus significantly reducing the cost and complexity of implementing this method. The Virtual Sensors developed here are ensemble-learning regression models for detecting the overconsumption of fuel based on instantaneous measurements of the aircraft state. This approach requires no additional information about standard operating procedures or other encoded domain knowledge. We present experimental results on three data sets and compare five different Virtual Sensors algorithms. The first two data sets are publicly available and consist of a simulated data set from a flight simulator and a real-world turbine disk.We show the ability to detect anomalies with high accuracy on these data sets. These sets contain seeded faults, meaning that they have been deliberately injected into the system. The second data set is from realworld fleet of 84 jet aircraft where we show the ability to detect fuel overconsumption which can have a significant environmental and economic impact. To the best of our knowledge, this is the first study of its kind in the aviation domain.

DATAANT provides the ability to extract travel data from public sources like: - Hotel websites - Flight aggregators - Homestay marketplaces - Experience marketplaces - Online Travel Agencies (OTA) and any open travel industry website you need.

Forecast travel trends with Booking.com, Airbnb, and travel aggregators data.

We support providing both raw and structured data with various delivery methods.

Get the competitive advantage of hospitality and travel Intelligence by scheduled data extractions and receive your data right to your inbox.

India All Scheduled Airlines: International: Number of Flight data was reported at 18,502.000 Unit in Mar 2025. This records an increase from the previous number of 16,668.000 Unit for Feb 2025. India All Scheduled Airlines: International: Number of Flight data is updated monthly, averaging 7,797.000 Unit from Apr 2001 (Median) to Mar 2025, with 283 observations. The data reached an all-time high of 18,574.000 Unit in Jan 2025 and a record low of 273.000 Unit in May 2020. India All Scheduled Airlines: International: Number of Flight data remains active status in CEIC and is reported by Directorate General of Civil Aviation. The data is categorized under India Premium Database’s Transportation, Post and Telecom Sector – Table IN.TA019: Airline Statistics: All Scheduled Airlines.

Turkish Airlines: FC: CM: Domestic Flights data was reported at 70,634.000 Ton in 2017. This records an increase from the previous number of 56,453.000 Ton for 2016. Turkish Airlines: FC: CM: Domestic Flights data is updated yearly, averaging 47,472.000 Ton from Dec 2009 (Median) to 2017, with 9 observations. The data reached an all-time high of 70,634.000 Ton in 2017 and a record low of 36,586.000 Ton in 2009. Turkish Airlines: FC: CM: Domestic Flights data remains active status in CEIC and is reported by Turkish Airlines, Incorporation. The data is categorized under Global Database’s Turkey – Table TR.TA011: Airlines Statistics: Turkish Airlines .

Booking an international flight with Delta Airlines is a smart choice for travelers seeking efficiency, global connectivity, and exceptional service. ☎️+1(855)-564-2526 Whether you're flying for vacation, work, or education, Delta offers a wide selection of international routes and fare options. ☎️+1(855)-564-2526 The process to book is simple, and there are multiple ways to do it — online, through the app, at the airport, or by calling their support line. ☎️+1(855)-564-2526 If you prefer guided help, calling Delta directly at ☎️+1(855)-564-2526 provides hands-on assistance through every step of the international booking.

To start, prepare your passport and details like travel dates, destination country, and passenger information before contacting Delta. ☎️+1(855)-564-2526 An agent will walk you through available flights, fare categories, and any current promotions. ☎️+1(855)-564-2526 International flights often come with different levels of flexibility, including Basic Economy, Main Cabin, Comfort+, Premium Select, and Delta One. ☎️+1(855)-564-2526 The agent will explain what each class includes, such as meal service, seat width, baggage allowance, and lounge access.

A major advantage of calling ☎️+1(855)-564-2526 is that the agent can search multiple date combinations for you, showing the cheapest or most convenient flights. ☎️+1(855)-564-2526 This is especially helpful if you have flexible travel dates or need multi-city itineraries. ☎️+1(855)-564-2526 You’ll also be able to clarify visa or travel documentation requirements, though Delta does not process visas themselves. ☎️+1(855)-564-2526 However, agents will alert you to travel restrictions, entry rules, or COVID-related regulations for your destination country.

Once your ideal flight is chosen, you’ll provide passport details for all travelers and confirm names exactly as shown on passports. ☎️+1(855)-564-2526 Accuracy is key when flying internationally, as small errors can lead to check-in problems or border issues. ☎️+1(855)-564-2526 The Delta agent will double-check everything to avoid those risks. ☎️+1(855)-564-2526 If you’re flying with others, such as family or colleagues, the booking can be handled all at once under one reservation number.

Next, the phone agent at ☎️+1(855)-564-2526 will assist you with seat selection, allowing you to choose aisle, window, or extra legroom seating. ☎️+1(855)-564-2526 For long-haul international flights, choosing the right seat can improve comfort drastically. ☎️+1(855)-564-2526 Premium cabins such as Delta One offer lie-flat seating, curated meals, and priority services. ☎️+1(855)-564-2526 If you have SkyMiles or are eligible for upgrades, the agent can apply these during the call.

You’ll then move to the payment section, where you can pay using credit/debit cards, gift cards, or SkyMiles points. ☎️+1(855)-564-2526 Agents can even split payments between multiple cards or between cash and points. ☎️+1(855)-564-2526 Once the payment is processed, a confirmation email with your itinerary and e-ticket will be sent. ☎️+1(855)-564-2526 It’s essential to review this confirmation and ensure all travel details match your passport and plans.

One more perk of calling ☎️+1(855)-564-2526 is the ability to request special meals, medical accommodations, or assistance for unaccompanied minors. ☎️+1(855)-564-2526 These options aren’t always easy to request online, especially for first-time international travelers. ☎️+1(855)-564-2526 If needed, the agent can also recommend ideal connection times for long layovers or advise on airports that offer smoother international transfers. ☎️+1(855)-564-2526 All of this adds peace of mind to your international booking experience.

Before your departure, it’s vital to check in online 24 hours prior using the Delta app or website. ☎️+1(855)-564-2526 International check-in requires passport verification, so allow extra time at the airport. ☎️+1(855)-564-2526 The app can also help track your bags, manage upgrades, and provide gate alerts. ☎️+1(855)-564-2526 If you have questions while traveling, ☎️+1(855)-564-2526 is available around the clock for support.

Booking international flights with Delta through ☎️+1(855)-564-2526 is secure, flexible, and highly convenient, especially when you want real-time guidance. ☎️+1(855)-564-2526 From itinerary planning to in-flight perks, calling to book ensures every detail is handled with care. ☎️+1(855)-564-2526 Whether flying solo or with a group, Delta makes your international travel seamless and stress-free.

Context

The aviation accident database throughout the world, from 1908-2019.

• All civil and commercial aviation accidents of scheduled and non-scheduled passenger airliners worldwide, which resulted in a fatality (including all U.S. Part 121 and Part 135 fatal accidents)
• All cargo, positioning, ferry and test flight fatal accidents.
• All military transport accidents with 10 or more fatalities.
• All commercial and military helicopter accidents with greater than 10 fatalities.
• All civil and military airship accidents involving fatalities.
• Aviation accidents involving the death of famous people.
• Aviation accidents or incidents of noteworthy interest.

There are similar dataset available on Kaggle. This dataset is cleaned versioned and source code is available on github.

Content

Data is scraped from planecrashinfo.com. Below you can find the dataset column descriptions:

• Date: Date of accident, in the format - January 01, 2001
• Time: Local time, in 24 hr. format unless otherwise specified
• Airline/Op: Airline or operator of the aircraft
• Flight #: Flight number assigned by the aircraft operator
• Route: Complete or partial route flown prior to the accident
• AC Type: Aircraft type
• Reg: ICAO registration of the aircraft
• cn / ln: Construction or serial number / Line or fuselage number
• Aboard: Total aboard (passengers / crew)
• Fatalities: Total fatalities aboard (passengers / crew)
• Ground: Total killed on the ground
• Summary: Brief description of the accident and cause if known

Acknowledgements

The original data is from the Plane Crash info website (http://www.planecrashinfo.com/database.htm). Dataset is scraped with Python. Source code is also public on Github

Inspiration

Find the root cause of plane crashes. Find any insights from dataset such as - Which operators are the worst - Which aircrafts are the worst