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 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.

Dataset

This dataset was created by bhoomika

Released under Database: Open Database, Contents: Database Contents

Contents

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...

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.

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 ---

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.

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 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"). You can download the non-aggregated results (55,000 rows) here.

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.

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

The 2009 ASA Statistical Computing and Graphics Data Expo consisted of flight arrival and departure details for all commercial flights on major carriers within the USA, from October 1987 to April 2008. This is a large dataset containing nearly 120 million records in total.

The aim of the data expo is to provide a graphical summary of important features of the data set. This is intentionally vague in order to allow different entries to focus on different aspects of the data, but here are a few ideas to get you started: •When is the best time of day, day of the week, and time of year to fly to minimise delays? •Do older planes suffer more delays? •How well does weather predict plane delays? •How does the number of people flying between different locations change over time? •Can you detect cascading failures as delays in one airport create delays in others? Are there critical links in the system? •Use the available variables to construct a model that predicts delays.

This dataset captures customer feedback for Singapore Airlines based on user reviews from various platforms. It provides insights into the customer experience, including service quality, seat comfort, food, and staff behaviour. The dataset includes structured fields that can be used to analyse customer satisfaction, identify pain points, and benchmark airline performance.

Dataset Features:

• S_ID: A unique identifier for each review.
• Published Date: The date and time when the review was published, formatted as ISO 8601.
• Published Platform: The platform or device from which the review was published (e.g., Desktop, Mobile).
• Rating: The numerical rating provided by the reviewer, typically ranging from 0 to 5.
• Type: Indicates the type of feedback, e.g., "review."
• Text: The detailed review content provided by the user describes their experience.
• Title: A short, descriptive summary of the review.
• Helpful Votes: The number of users who found the review helpful.

Usage:

This dataset is valuable for: - Sentiment Analysis: Understanding customer satisfaction and sentiment trends. - Topic Modeling: Identifying common themes or issues in customer reviews. - Predictive Analytics: Forecasting customer ratings based on review content. - Service Improvement: Pinpointing areas for operational or service enhancements.

Coverage:

The dataset provides a comprehensive view of customer experiences across different dates, platforms, and review ratings. It captures both positive and negative feedback, offering a balanced dataset for various analytical purposes.

License:

CC0 (Public Domain)

Who Can Use It:

This dataset is suitable for data scientists, machine learning practitioners, researchers, airline industry analysts, and students interested in customer feedback analysis.

How to Use It:

• Develop text classification models to predict ratings or review helpfulness.
• Perform natural language processing (NLP) tasks to extract key insights.
• Create dashboards or visualisations to monitor customer satisfaction trends.
• Benchmark airline performance by comparing it with reviews from competitors.

This report describes a cooperative experiment conducted by ONERA and NASA, with the support of Airbus S.A.S. and easyJet Airline Company, Ltd. The study evaluated the benefits of two distinctly different methodologies for analyzing the same set of digital flight-recorded data. The experiment analyzed a set of easyJet commercial-flight data with both typical Flight Operational Quality Assur-ance (FOQA) software of an airline (in this case, AirFASE, developed by Airbus and Teledyne) and The Morning Report of Atypical Flights (developed by NASA). The study demonstrated the feasibility and potential value of using The Morning Report tool in conjunction with the FOQA airline tool and also showed the complementarities of the results produced by the two approaches.

A. SUMMARY This dataset consists of San Francisco International Airport (SFO) The aircraft landing dataset contains data about aircraft landings at SFO with monthly landing counts and landed weight by airline, region and aircraft model and type. B. HOW THE DATASET IS CREATED Data is self-reported by airlines and is only available at a monthly level. C. UPDATE PROCESS Data is available starting in July 1999 and will be updated monthly. 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, Cargo Statistics belonging to United Airlines will appear in multiple attribute fields and are additive, which provides flexibility for the user to derive categorical Cargo Statistics as desired. E. RELATED DATASETS A summary of monthly comparative air-traffic statistics is also available on SFO’s internet site at https://www.flysfo.com/about/media/facts-statistics/air-traffic-statistics

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.

A. SUMMARY This dataset consists of San Francisco International Airport (SFO) air traffic cargo dataset contains data about cargo volume into and out of SFO, in both metric tons and pounds, with monthly totals by airline, region and aircraft type. B. HOW THE DATASET IS CREATED Data is self-reported by airlines and is only available at a monthly level. C. UPDATE PROCESS Data is available starting in July 1999 and will be updated monthly. 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, Cargo Statistics belonging to United Airlines will appear in multiple attribute fields and are additive, which provides flexibility for the user to derive categorical Cargo Statistics as desired. E. RELATED DATASETS A summary of monthly comparative air-traffic statistics is also available on SFO’s internet site at https://www.flysfo.com/about/media/facts-statistics/air-traffic-statistics

This dataset provides 2200 honest text reviews concerning Emirates Airline, sourced from the Skytrax platform [1]. Skytrax is a United Kingdom-based consultancy renowned for its airline and airport review and ranking website [1]. The dataset's primary purpose is to facilitate data science and analytics initiatives, offering valuable insights into customer experiences within the travel sector [1]. It is particularly suitable for applications in data analytics, travel industry analysis, data visualisation, exploratory data analysis, and natural language processing (NLP) [1].

Columns

The dataset contains two distinct columns [2]. The first column is an index, serving as a unique identifier for each review [2]. The second column, named 'reviews', contains the actual text-based customer feedback [2].

Distribution

The dataset comprises 2200 individual customer reviews [1]. It is structured with two columns [2]. While specific file formats are not detailed in the provided data, such datasets are typically provided in CSV format [3]. Each 'label count' interval contains 220 entries, summing up to the total review count [2]. There are also 2199 unique values in the index and 2194 unique review texts [2].

Usage

This dataset is ideal for various applications, including [1]: * Data Science and Analytics: For deriving actionable insights from customer feedback. * Travel Industry Analysis: To understand passenger sentiments regarding Emirates Airline. * Data Visualisation: For creating visual representations of review patterns and trends. * Exploratory Data Analysis (EDA): To discover initial patterns, spot anomalies, and test hypotheses. * Natural Language Processing (NLP): For tasks such as sentiment analysis, topic modelling, and text classification of customer reviews.

Coverage

The reviews within this dataset pertain to Emirates Airline, with the data being of global relevance [4]. The Skytrax platform, from which the reviews originate, is a United Kingdom-based consultancy [1]. The dataset was listed on 17 June 2025 [4].

License

CCO

Who Can Use It

This dataset is intended for a range of users interested in airline customer feedback and text analysis [1]: * Data Scientists: For building predictive models or understanding customer sentiment. * Data Analysts: For generating reports and identifying trends in customer satisfaction. * Researchers: Studying consumer behaviour in the travel industry. * Natural Language Processing Practitioners: Developing and testing NLP models for sentiment analysis or text mining. * Travel Industry Stakeholders: Gaining insights into service quality and areas for improvement.

Dataset Name Suggestions

• Emirates Airline Customer Reviews
• Skytrax Emirates Airline Reviews Dataset
• Emirates Customer Feedback Data
• Airline Review Data for Analytics

Attributes

Original Data Source: Emirates Reviews Skytrax

The worldwide civilian aviation system is one of the most complex dynamical systems created. Most modern commercial aircraft have onboard flight data recorders that record several hundred discrete and continuous parameters at approximately 1Hz for the entire duration of the flight. These data contain information about the flight control systems, actuators, engines, landing gear, avionics, and pilot commands. In this paper, recent advances in the development of a novel knowledge discovery process consisting of a suite of data mining techniques for identifying precursors to aviation safety incidents are discussed. The data mining techniques include scalable multiple-kernel learning for large-scale distributed anomaly detection. A novel multivariate time-series search algorithm is used to search for signatures of discovered anomalies on massive datasets. The process can identify operationally significant events due to environmental, mechanical, and human factors issues in the high-dimensional flight operations quality assurance data. All discovered anomalies are validated by a team of independent domain experts. This novel automated knowledge discovery process is aimed at complementing the state-of-the-art human-generated exceedance-based analysis that fails to discover previously unknown aviation safety incidents. In this paper, the discovery pipeline, the methods used, and some of the significant anomalies detected on real-world commercial aviation data are discussed.