We'll tailor a bespoke airline dataset to meet your unique needs, encompassing flight details, destinations, pricing, passenger reviews, on-time performance, and other pertinent metrics.

Leverage our airline datasets for diverse applications to bolster strategic planning and market analysis. Scrutinizing these datasets enables organizations to grasp traveler preferences and industry trends, facilitating nuanced operational adaptations and marketing initiatives. Customize your access to the entire dataset or specific subsets as per your business requisites.

Popular use cases involve optimizing route profitability, improving passenger satisfaction, and conducting competitor analysis.

This dataset contains reviews of the top 10 rated airlines in 2023 sourced from the Airline Quality (https://www.airlinequality.com) website. The reviews cover various aspects of the flight experience, including seat comfort, staff service, food and beverages, inflight entertainment, value for money, and overall rating. The dataset is suitable for sentiment analysis, customer satisfaction analysis, and other similar tasks.

Usage - Download the dataset file airlines_reviews.csv. - Use the dataset for analysis, visualization, and machine learning tasks.

List of Airlines 1. Singapore Airlines 2. Qatar Airways 3. All Nippon Airways 4. Emirates 5. Japan Airlines 6. Turkish Airlines 7. Air France 8. Cathay Pacific Airways 9. EVA Air 10.Korean Air

This dataset is provided under the MIT License.

Dataset

This dataset was created by bhoomika

Released under Database: Open Database, Contents: Database Contents

Contents

Dataset Card for Twitter US Airline Sentiment

  Dataset Summary

This data originally came from Crowdflower's Data for Everyone library. As the original source says,

A sentiment analysis job about the problems of each major U.S. airline. Twitter data was scraped from February of 2015 and contributors were asked to first classify positive, negative, and neutral tweets, followed by categorizing negative reasons (such as "late flight" or "rude service").

The data we're… See the full description on the dataset page: https://huggingface.co/datasets/osanseviero/twitter-airline-sentiment.

Analysis of ‘Panel Dataset / Cost Data of U.S. Airlines’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/sandhyakrishnan02/paneldata on 28 January 2022.

--- Dataset description provided by original source is as follows ---

Dataset Details:

Data Set contains Cost Data for U.S. Airlines, 90 Observations On 6 Firms For 15 Years, 1970-1984

Predictors:

I = Airline, T = Year, Q = Output, in revenue passenger miles, index number, PF = Fuel price, LF = Load factor, the average capacity utilization of the fleet.

Response:

C = Total cost, in $1000,

Acknowledgements and Credit

These data are a subset of a larger data set provided to the author by Professor Moshe Kim. They were originally constructed by Christensen Associates of Madison, Wisconsin.

Inspiration

Perform various econometric analyses to check which model suits best for the given dataset. To start with can check this notebook which is programmed in R.

--- Original source retains full ownership of the source dataset ---

Success.ai’s Aviation Data provides verified access to professionals across the airlines, aviation, and aerospace industries. Leveraging over 700 million LinkedIn profiles, this dataset delivers actionable insights, contact details, and firmographic data for pilots, engineers, airline executives, aerospace manufacturers, and more. Whether your goal is to market aviation technology, recruit aerospace specialists, or analyze industry trends, Success.ai ensures your outreach is powered by accurate, enriched, and continuously updated data.

Why Choose Success.ai’s Aviation Data? Comprehensive Professional Profiles

Access verified LinkedIn profiles of pilots, engineers, flight operations managers, safety specialists, and aviation executives. AI-driven validation ensures 99% accuracy, reducing bounce rates and enhancing communication efficiency. Global Coverage Across Aviation and Aerospace Sectors

Includes professionals from airlines, airport authorities, aerospace manufacturers, and aviation technology providers. Covers key regions such as North America, Europe, APAC, South America, and the Middle East. Continuously Updated Dataset

Real-time updates reflect changes in roles, organizational affiliations, and professional achievements, ensuring relevant targeting. Tailored for Aviation and Aerospace Insights

Enriched profiles include work histories, areas of specialization, professional certifications, and firmographic data. Data Highlights: 700M+ Verified LinkedIn Profiles: Access a vast network of aviation and aerospace professionals worldwide. 100M+ Work Emails: Communicate directly with pilots, engineers, and airline executives. Enriched Professional Histories: Gain insights into career paths, certifications, and organizational roles. Industry-Specific Segmentation: Target professionals in commercial aviation, aerospace R&D, airport management, and more with precision filters. Key Features of the Dataset: Aviation and Aerospace Professional Profiles

Identify and connect with airline CEOs, aerospace engineers, maintenance technicians, flight safety experts, and other key professionals. Engage with individuals responsible for operational decisions, technology adoption, and aviation safety protocols. Detailed Firmographic Data

Leverage insights into company sizes, fleet compositions, geographic operations, and market focus. Align outreach to match specific industry needs and organizational scales. Advanced Filters for Precision Targeting

Refine searches by region, job role, certifications (e.g., FAA, EASA), or years of experience for tailored outreach. Customize campaigns to address unique aviation challenges such as sustainability, fleet modernization, or safety compliance. AI-Driven Enrichment

Enhanced datasets provide actionable insights for personalized campaigns, highlighting certifications, achievements, and career milestones. Strategic Use Cases: Marketing Aviation Products and Services

Promote aviation technology, flight operations software, or aerospace equipment to airline operators and engineers. Engage with professionals responsible for procurement, fleet management, and airport operations. Recruitment and Talent Acquisition

Target HR professionals and aerospace manufacturers seeking pilots, engineers, and aviation specialists. Simplify hiring for roles requiring advanced technical expertise or certifications. Collaboration and Partnerships

Identify aerospace manufacturers, airlines, or airport authorities for joint ventures, technology development, or service agreements. Build partnerships with key players driving innovation and safety in aviation. Market Research and Industry Analysis

Analyze trends in airline operations, aerospace manufacturing, and aviation technology to inform strategy. Use insights to refine product development and marketing efforts tailored to the aviation industry. Why Choose Success.ai? Best Price Guarantee

Access high-quality Aviation Data at unmatched pricing, ensuring cost-effective campaigns and strategies. Seamless Integration

Easily integrate verified aviation data into CRMs, recruitment platforms, or marketing systems using APIs or downloadable formats. AI-Validated Accuracy

Depend on 99% accurate data to minimize wasted efforts and maximize engagement with aviation professionals. Customizable Solutions

Tailor datasets to specific aviation sectors, geographic regions, or professional roles to meet your strategic objectives. Strategic APIs for Enhanced Campaigns: Data Enrichment API

Enhance existing records with verified aviation profiles to refine targeting and engagement. Lead Generation API

Automate lead generation for a consistent pipeline of qualified professionals in the aviation sector, scaling your outreach efficiently. Success.ai’s Aviation Data empowers you to connect with the leaders and innovators shaping the aviation and aerospace industries. With verified conta...

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.

Air travel is one of the most used ways of transit in our daily lives. So it's no wonder that more and more people are sharing their experiences with airlines and airports using web-based online surveys. This dataset aims to do topic modeling and sentiment analysis on Skytrax (airlinequality.com) and Tripadvisor (tripadvisor.com) postings where there is a lot of interest and engagement from people who have used it or want to use it for airlines.

A. SUMMARY San Francisco International Airport Report on Monthly Passenger Traffic Statistics by Airline. B. HOW THE DATASET IS CREATED Data is self-reported by airlines and is only available at a monthly level C. UPDATE PROCESS Data updated quarterly D. HOW TO USE THIS DATASET Airport data is seasonal in nature, therefore any comparative analyses should be done on a period-over-period basis (i.e. January 2010 vs. January 2009) as opposed to period-to-period (i.e. January 2010 vs. February 2010). It is also important to note that fact and attribute field relationships are not always 1-to-1. For example, Passenger Counts belonging to United Airlines will appear in multiple attribute fields and are additive, which provides flexibility for the user to derive categorical Passenger Counts as desired.

1) Data Introduction • The Airline Passenger Satisfaction dataset contains the Airline Passenger Satisfaction Survey, an important challenge for airlines.

2) Data Utilization (1) Airline Passenger Satisfaction data has characteristics that: • The dataset includes 24 variables such as gender, consumer type, age, travel type, boarding class, and flight distance, and 20% is test data. (2) Airline Passenger Satisfaction data can be used to: • Factor Analysis: Navigating data to help identify key differences between satisfied and unsatisfied passengers. • Characteristic Importance Analysis: Build a satisfaction prediction model by analyzing the importance of various factors.

Comprehensive dataset of 294 Airlines in New York, United States as of July, 2025. Includes verified contact information (email, phone), geocoded addresses, customer ratings, reviews, business categories, and operational details. Perfect for market research, lead generation, competitive analysis, and business intelligence. Download a complimentary sample to evaluate data quality and completeness.

Airline Passenger Reviews Dataset This dataset contains real-world airline passenger reviews gathered from various flights across different countries and airlines. Each row represents an individual passenger’s experience and feedback on a specific flight. Dataset Overview The dataset includes reviews on several aspects of the airline experience, such as seat comfort, food, cabin crew service, value for money, and more. It can be used for sentiment analysis, NLP-based text classification, airline performance evaluation, and other machine learning or data visualization tasks.

Dataset Overview

Time Period: January 2016 to December 2024.

Source: Manila International Airport Authority (MIAA).

Purpose: Analyze flight and passenger trends at NAIA, including the impact of COVID-19 and recovery patterns.

Columns Description*

date: The date of the record (monthly data).

departing_international_flights: Number of international flights departing from NAIA.

arriving_international_flights: Number of international flights arriving at NAIA.

departing_international_passengers: Number of passengers on departing international flights.

arriving_international_passengers: Number of passengers on arriving international flights.

**departing_domestic_flights: ** Number of domestic flights departing from NAIA.

arriving_domestic_flights: Number of domestic flights arriving at NAIA.

departing_domestic_passengers: Number of passengers on departing domestic flights.

arriving_domestic_passengers: Number of passengers on arriving domestic flights.

The U.S. Department of Transportation's (DOT) Bureau of Transportation Statistics (BTS) tracks the on-time performance of domestic flights operated by large air carriers. Summary information on the number of on-time, delayed, canceled and diverted flights appears in DOT's monthly Air Travel Consumer Report, published about 30 days after the month's end, as well as in summary tables posted on this website. BTS began collecting details on the causes of flight delays in June 2003. Summary statistics and raw data are made available to the public at the time the Air Travel Consumer Report is released.

This version of the dataset was compiled from the Statistical Computing Statistical Graphics 2009 Data Expo and is also available here.

What is Data Expo 2009 - Airline on-time performance?

Have you ever been stuck in an airport because your flight was delayed or canceled and wondered if you could have predicted it if you'd had more data? This is your chance to find out.

.

How to use this dataset

We had a total of nine entries, and turn ou at the poster session at the JSM was great, with plenty of people stopping by to find out why their flights were delayed.

Acknowledgments

When we use this dataset in our research, we credit the authors.

• License : CC BY 4.0.

• This data set is taken from Harvard Dataset- Data Expo 2009: Airline on time data

The main idea for uploading this dataset is to practice data analysis with my students, as I am working in college and want my student to train our studying ideas in a big dataset, It may be not up to date and I mention the collecting years, but it is a good resource of data to practice

Context

The U.S. Department of Transportation's (DOT) Bureau of Transportation Statistics tracks the on-time performance of domestic flights operated by large air carriers. I came across this useful data from DOT's database at working and figured this would be a really helpful dataset: Summary information on the number of on-time, delayed, canceled, and diverted flight.

Content

The datasets contain daily airline information covering from flight information, carrier company, to taxing-in, taxing-out time, and generalized delay reason of exactly 10 years, from 2009 to 2019. The DOT's database is renewed from 2018, so there might be a minor change in the column names.

Acknowledgements

The flight delay and cancellation data were collected and managed by the DOT's Bureau of Transportation Statistics, only included data related to time-analysis on each flight. For any inspiration, please see tasks.

Previous research described the use of machine learning algorithms to predict aircraft fuel consumption. This technique, known as Virtual Sensors, models fuel consumption as a function of aircraft Flight Operations Quality Assurance (FOQA) data. FOQA data consist of a large number of measurements that are already recorded by many commercial airlines. The predictive model is used for anomaly detection in the fuel consumption history by noting when measured fuel consumption exceeds an expected value. This exceedance may indicate overconsumption of fuel, the source of which may be identified and corrected by the aircraft operator. This would reduce both fuel emissions and operational costs. This paper gives a brief overview of the modeling approach and describes efforts to validate and analyze the initial results of this project. We examine the typical error in modeling, and compare modeling accuracy against both complex and simplistic regression approaches. We also estimate a ranking of the importance of each FOQA variable used as input, and demonstrate that FOQA variables can reliably be used to identify different modes of fuel consumption, which may be useful in future work. Analysis indicates that fuel consumption is accurately predicted while remaining theoretically sensitive to sub-nominal pilot inputs and maintenance-related issues.

Dataset

This dataset was created by Oliver Collins

Released under CC0: Public Domain

Contents

For the purposes of this paper, the National Airspace System (NAS) encompasses the operations of all aircraft which are subject to air traffic control procedures. The NAS is a highly complex dynamic system that is sensitive to aeronautical decision-making and risk management skills. In order to ensure a healthy system with safe flights a systematic approach to anomaly detection is very important when evaluating a given set of circumstances and for determination of the best possible course of action. Given the fact that the NAS is a vast and loosely integrated network of systems, it requires improved safety assurance capabilities to maintain an extremely low accident rate under increasingly dense operating conditions. Data mining based tools and techniques are required to support and aid operators’ (such as pilots, management, or policy makers) overall decision-making capacity. Within the NAS, the ability to analyze fleetwide aircraft data autonomously is still considered a significantly challenging task. For our purposes a fleet is defined as a group of aircraft sharing generally compatible parameter lists. Here, in this effort, we aim at developing a system level analysis scheme. In this paper we address the capability for detection of fleetwide anomalies as they occur, which itself is an important initiative toward the safety of the real-world flight operations. The flight data recorders archive millions of data points with valuable information on flights everyday. The operational parameters consist of both continuous and discrete (binary & categorical) data from several critical subsystems and numerous complex procedures. In this paper, we discuss a system level anomaly detection approach based on the theory of kernel learning to detect potential safety anomalies in a very large data base of commercial aircraft. We also demonstrate that the proposed approach uncovers some operationally significant events due to environmental, mechanical, and human factors issues in high dimensional, multivariate Flight Operations Quality Assurance (FOQA) data. We present the results of our detection algorithms on real FOQA data from a regional carrier.

Mining opinions from reviews has been a field of ever-growing research. These include mining opinions on document level, sentence-level, and even aspect level of a review. While explicitly mentioned aspects in a review have been widely researched, very little work has been done in gathering opinions on aspects that are implied and not explicitly mentioned. E.g. “the flight was spacious and there was plenty of legroom”. This gives an opinion on the entities of the cabin and seat of an airline. Words like “spacious” and phrases like “plenty of legroom” help identify these implied entities and the opinions attached to them. Not much research has been done for gathering such implicit aspects and opinions for airline reviews. The present dataset is a manually annotated domain-specific aspect-based corpus that helps a study to extract and analyze opinions about such implied aspects and entities of airlines.