Tourist Arrivals in the United States increased to 6893068 in August from 6275257 in July of 2025. This dataset provides - United States Tourist Arrivals- actual values, historical data, forecast, chart, statistics, economic calendar and news.

United States US: International Tourism: Number of Arrivals data was reported at 75,608,000.000 Person in 2016. This records a decrease from the previous number of 77,465,000.000 Person for 2015. United States US: International Tourism: Number of Arrivals data is updated yearly, averaging 51,107,500.000 Person from Dec 1995 (Median) to 2016, with 22 observations. The data reached an all-time high of 77,465,000.000 Person in 2015 and a record low of 41,218,000.000 Person in 2003. United States US: International Tourism: Number of Arrivals data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s United States – Table US.World Bank.WDI: Tourism Statistics. International inbound tourists (overnight visitors) are the number of tourists who travel to a country other than that in which they have their usual residence, but outside their usual environment, for a period not exceeding 12 months and whose main purpose in visiting is other than an activity remunerated from within the country visited. When data on number of tourists are not available, the number of visitors, which includes tourists, same-day visitors, cruise passengers, and crew members, is shown instead. Sources and collection methods for arrivals differ across countries. In some cases data are from border statistics (police, immigration, and the like) and supplemented by border surveys. In other cases data are from tourism accommodation establishments. For some countries number of arrivals is limited to arrivals by air and for others to arrivals staying in hotels. Some countries include arrivals of nationals residing abroad while others do not. Caution should thus be used in comparing arrivals across countries. The data on inbound tourists refer to the number of arrivals, not to the number of people traveling. Thus a person who makes several trips to a country during a given period is counted each time as a new arrival.; ; World Tourism Organization, Yearbook of Tourism Statistics, Compendium of Tourism Statistics and data files.; Gap-filled total;

United States US: International Tourism: Number of Departures data was reported at 73,453,000.000 Person in 2015. This records an increase from the previous number of 68,176,000.000 Person for 2014. United States US: International Tourism: Number of Departures data is updated yearly, averaging 61,061,000.000 Person from Dec 1995 (Median) to 2015, with 21 observations. The data reached an all-time high of 73,453,000.000 Person in 2015 and a record low of 51,285,000.000 Person in 1995. United States US: International Tourism: Number of Departures data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s United States – Table US.World Bank.WDI: Tourism Statistics. International outbound tourists are the number of departures that people make from their country of usual residence to any other country for any purpose other than a remunerated activity in the country visited. The data on outbound tourists refer to the number of departures, not to the number of people traveling. Thus a person who makes several trips from a country during a given period is counted each time as a new departure.; ; World Tourism Organization, Yearbook of Tourism Statistics, Compendium of Tourism Statistics and data files.; Gap-filled total;

India Visitors Arrivals: North America: USA data was reported at 1,376,919.000 Person in 2017. This records an increase from the previous number of 1,296,939.000 Person for 2016. India Visitors Arrivals: North America: USA data is updated yearly, averaging 251,926.000 Person from Dec 1981 (Median) to 2017, with 37 observations. The data reached an all-time high of 1,376,919.000 Person in 2017 and a record low of 82,052.000 Person in 1981. India Visitors Arrivals: North America: USA data remains active status in CEIC and is reported by Ministry of Tourism. The data is categorized under Global Database’s India – Table IN.QB002: Foreign Tourist Arrivals: by Countries (Annual).

Dataset Overview 📝

This dataset includes key tourism and economic indicators for over 200 countries, spanning the years from 1999 to 2023. It covers a wide range of data related to tourism arrivals, expenditures, receipts, GDP, unemployment, and inflation, helping to explore the relationship between tourism and economic growth globally.

Tourism & Economic Indicators:

• tourism_receipts 💰: The total income a country generates from international tourism, measured in current US dollars.
• tourism_arrivals 🌍: The total number of international tourists who arrive in a country, measured in count.
• tourism_expenditures 🛍️: The amount of money spent by international tourists within the country, measured in current US dollars.
• tourism_exports 📈: The percentage of a country’s total exports derived from international tourism receipts.
• tourism_departures ✈️: The number of citizens or residents of a country who travel abroad for tourism.
• tourism_expenditures 🛫: The percentage of a country’s total imports spent on international tourism.
• gdp 📊: The total value of all goods and services produced in a country, expressed in current US dollars.
• inflation 📉: The annual percentage change in the average price of goods and services in a country.
• unemployment 👷‍♂️: The percentage of people within the labor force who are unemployed but actively seeking work.

Data Source 🌐

• World Bank: The dataset is sourced from the World Bank’s economic and tourism databases, offering reliable and up-to-date statistics on global tourism and economic indicators.

Potential Use Cases 🔍

• Tourism & Economic Impact Analysis 🌏: Analyzing the contribution of tourism to a country’s GDP, as well as how tourism spending impacts national economies.
• Cross-Country Comparison 📊: Comparing tourism arrivals, expenditures, and receipts relative to GDP and unemployment across countries and regions.
• Predictive Modeling 🤖: Building models to predict the future impact of tourism on economic growth and identify emerging trends.
• Policy Evaluation 🏛️: Helping policymakers assess the role of tourism in economic planning, especially regarding inflation and unemployment.
• Economic Forecasting 📈: Using historical data to forecast how tourism will influence economic conditions, helping in the development of economic policies.

Key Questions You Can Explore 🤔

• How do tourism receipts correlate with GDP growth in different countries? 💵
• What is the relationship between tourism expenditure and inflation rates? 📉
• How do tourism arrivals and departures vary by region and what are the key drivers? 🌍
• What role does tourism play in shaping unemployment rates across countries? 👥
• Which countries have seen the largest increase in tourism receipts over the past two decades? 📊

Important Notes ⚠️

• Missing Data 🚨: Some values may be missing for certain years or countries, especially for specific tourism indicators. Techniques like forward filling, backward filling, or interpolation may help in handling missing values during time series analysis.
• Currency & Inflation Adjustments 💲: When comparing tourism receipts and expenditures across countries, consider adjusting for inflation and exchange rates to ensure accurate year-on-year comparisons.

India Foreign Tourist Arrivals: United States data was reported at 175,872.000 Person in Feb 2025. This records a decrease from the previous number of 197,142.000 Person for Jan 2025. India Foreign Tourist Arrivals: United States data is updated monthly, averaging 118,735.056 Person from Feb 2017 (Median) to Feb 2025, with 81 observations. The data reached an all-time high of 250,812.907 Person in Dec 2024 and a record low of 4,360.159 Person in May 2021. India Foreign Tourist Arrivals: United States data remains active status in CEIC and is reported by CEIC Data. The data is categorized under India Premium Database’s Tourism Sector – Table IN.QB001: Foreign Tourist Arrivals: by Countries.

This dataset provides detailed information on flight arrivals and delays for U.S. airports, categorized by carriers. The data includes metrics such as the number of arriving flights, delays over 15 minutes, cancellation and diversion counts, and the breakdown of delays attributed to carriers, weather, NAS (National Airspace System), security, and late aircraft arrivals. Explore and analyze the performance of different carriers at various airports during this period. Use this dataset to gain insights into the factors contributing to delays in the aviation industry.

Purpose: The purpose of this dataset is to offer insights into the performance of U.S. carriers at various airports during August 2013 - August 2023, focusing on flight arrivals and delays. By providing detailed information on key metrics such as the number of arriving flights, delays over 15 minutes, cancellations, and diversions, the dataset aims to facilitate analyses of factors contributing to delays, including those attributed to carriers, weather, the National Airspace System (NAS), security, and late aircraft arrivals. Researchers, data scientists, and aviation enthusiasts can leverage this dataset to explore patterns, identify trends, and draw conclusions that contribute to a better understanding of the aviation industry's operational challenges.

Structure: The dataset is structured as a tabular format with rows representing unique combinations of year, month, carrier, and airport. Each row contains information on various metrics, including flight counts, delay counts, cancellation and diversion counts, and delay breakdowns by different factors. The columns provide specific details such as carrier codes and names, airport codes and names, and counts of delays attributed to carrier, weather, NAS, security, and late aircraft arrivals. The structured format ensures that users can easily query, analyze, and visualize the data to derive meaningful insights.

• year: The year of the data.
• month: The month of the data.
• carrier: Carrier code.
• carrier_name: Carrier name.
• airport: Airport code.
• airport_name: Airport name.
• arr_flights: Number of arriving flights.
• arr_del15: Number of flights delayed by 15 minutes or more.
• carrier_ct: Carrier count (delay due to the carrier).
• weather_ct: Weather count (delay due to weather).
• nas_ct: NAS (National Airspace System) count (delay due to the NAS).
• security_ct: Security count (delay due to security).
• late_aircraft_ct: Late aircraft count (delay due to late aircraft arrival).
• arr_cancelled: Number of flights canceled.
• arr_diverted: Number of flights diverted.
• arr_delay: Total arrival delay.
• carrier_delay: Delay attributed to the carrier.
• weather_delay: Delay attributed to weather.
• nas_delay: Delay attributed to the NAS.
• security_delay: Delay attributed to security.
• late_aircraft_delay: Delay attributed to late aircraft arrival.

Usage: Researchers, analysts, and data enthusiasts can utilize this dataset for a variety of purposes, including but not limited to:

Performance Analysis: Assess the on-time performance of different carriers at specific airports and identify potential areas for improvement.

Trend Identification: Analyze temporal trends in delays, cancellations, and diversions to understand whether certain months or periods exhibit higher operational challenges.

Root Cause Analysis: Investigate the primary contributors to delays, such as carrier-related issues, weather conditions, NAS inefficiencies, security concerns, or late aircraft arrivals.

Benchmarking: Compare the performance of various carriers across different airports to identify industry leaders and areas requiring attention.

Predictive Modeling: Use historical data to develop predictive models for flight delays, aiding in the development of strategies to mitigate disruptions.

Industry Insights: Contribute to a broader understanding of the factors influencing operational efficiency within the U.S. aviation sector.

As users explore and analyze the dataset, they can gain valuable insights that may inform decision-making processes, improve operational strategies, and contribute to a more efficient and reliable air travel experience.

Context

This dataset deals with the visitors of foreigners to INDIA.

It includes foreigners (not Indian), overseas Indian, and crew members, except for some of the foreign arrivals who are not considered tourists (diplomats, soldiers, permanent residents, visiting cohabitation, and residence).

The Indian Government has compiled, analyzed, and provided statistics on foreign tourists visiting Indian and overseas tourists by type.

The data materials were prepared for the purpose of utilizing them as basic data for establishing tourism policies and marketing strategies.

I created this dataset by rebuilding the data provided by the Indian Government for easy analysis.

Column Name and Explanation:

noftaii: No. of Foreign Tourist Arrivals in India (in Million) noftaiiagr: No. of Foreign Tourist Arrivals in India, Annual growth rate(in %)(compare to the previous year) noindfi: No.of Indian Nationals departures from India (in Million) noindfiagr: No.of Indian Nationals departures from India, Annual growth rate(in %)(compare to the previous year) nodtvasu: No. of Domestic Tourist Visits to all States/UTs nodtvasuagr: No. of Domestic Tourist Visits to all States/UTs feeftit: Estimated Foreign Exchange Earnings from Tourism in INR terms in Crores feeftitagr: Estimated Foreign Exchange Earnings from Tourism in INR terms, Annual growth rate(in %)(compare to the previous year) feeftust: Estimated Foreign Exchange Earnings from Tourism in US$ terms in Billions feeftustagr : Estimated Foreign Exchange Earnings from Tourism in US$ terms, Annual growth rate(in %)(compare to the previous year) wnoita: world level No. of International Tourist Arrivals in Millions wnoitaagr: world level No. of International Tourist Arrivals, Annual growth rate(in %)(compare to the previous year) witr: world level International Tourism Receipts in US$ Billion witragr: world level International Tourism Receipts in US$ Billion, Annual growth rate(in %)(compare to the previous year) aprnoita: In Asia and The Pacific Region, No. of International Tourist Arrivals in Million aprnoitaagr: In Asia and The Pacific Region, No. of International Tourist Arrivals in Million, Annual growth rate(in %)(compare to the previous year) apfitr: In Asia and The Pacific Region, International Tourism Receipts in US$ Billion apritragr: In Asia and The Pacific Region, International Tourism Receiptsin US$ Billion, Annual growth rate(in %)(compare to the previous year) ipwiita: India’s Position in World, Share of India in International Tourist Arrivals(in %) ipwirwta: India’s Position in World, India’s rank in World Tourist Arrivals ipwsiitr: India’s Position in World, Share of India in International Tourism Receipts (US$ terms) (in %) ipwirwtr: Position in Asia & the Pacific Region, India’s rank in World Tourism Receipts ipaprita: Position in Asia & the Pacific Region, Share of India in International Tourist Arrivals(in %)

Acknowledgments

Thanks to the Indian Ministry of Tourismfor making the data available to the general public. For more details, you can refer: https://github.com/kachhadiyaraj15/india_tourism_2014_2020

Cover Photo

Photo from Adobe Stock https://stock.adobe.com/in/images/collage-of-india-historical-monuments-architectural-buildings-and-ruins-a-india-tour-and-travel-banner-of-notable-tourist-destinations/142044867

The number of international tourist arrivals in Asia was forecast to continuously increase between 2024 and 2029 by in total 174.7 million arrivals (+33.49 percent). After the ninth consecutive increasing year, the arrivals is estimated to reach 696.34 million arrivals and therefore a new peak in 2029. Depicted is the number of inbound international tourists. According to World Bank this refers to tourists travelling to a country which is not their usual residence, whereby the main purpose is not work related and the planned visitation period does not exceed 12 months. The forecast has been adjusted for the expected impact of COVID-19.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in more than 150 countries and regions worldwide. All input data are sourced from international institutions, national statistical offices, and trade associations. All data has been are processed to generate comparable datasets (see supplementary notes under details for more information).Find more key insights for the number of international tourist arrivals in countries like North America and Caribbean.

U.S. volume visitation data (from overseas/all modes and Mexico/air and sea) are collected and reported by the National Travel and Tourism Office\'s (NTTO) ADIS/I-94 Visitor Arrivals Program (source: CBP/DHS ADIS/I-94 Record).

This project is about predicting if a flight will be delayed by over 15 minutes upon arrival, with Scikit-learn Decision Tree Classifier, using US flight data in 2022. Here is the URL of the dataset and variables description: https://www.transtats.bts.gov/DL_SelectFields.aspx?gnoyr_VQ=FGK&QO_fu146_anzr=b0-gvzr

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. This dataset is collected from the Bureau of Transportation Statistics, Govt. of the USA. This data is open-sourced under U.S. Govt. Works. I dowload 12 csv file that represents each month of 2022. This dataset contains all US domestic flights in 2022.

Description of Columns • Quarter Quarter (1-4) • Month Month • DayofMonth Day of Month • DayOfWeek Day of Week • FlightDate Date of the Flight • Marketing_Airline_Network Airline Identifier • OriginCityName Origin Airport, City Name • DestCityName Destination Airport, City Name • DepDelay Difference in minutes between scheduled and actual departure time. Early departures show negative numbers • ArrDelay Difference in minutes between scheduled and actual arrival time. Early arrivals show negative numbers • Cancelled Cancelled Flight (1=Yes) • Diverted Diverted Flight (1=Yes) • AirTime Flight Time, in Minutes • Distance Distance between airports (miles) • CarrierDelay Delay caused by the airline in minutes • WeatherDelay Delay caused by weather • NASDelay Delay caused by air system • SecurityDelay Delay caused by security reasons • LateAircraftDelay Delay caused as a result of another flight on the same aircraft delayed

Context

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 on-time data posted on this website.

Content

A flight is considered delayed if it is late by more than 15 minutes.

Factors considered: Year: The year of the flight (all records are from 2013) Month: The month of the flight DayofMonth: The day of the month on which the flight departed DayOfWeek: The day of the week on which the flight departed - from 1 (Monday) to 7 (Sunday) Carrier: The two-letter abbreviation for the airline. OriginAirportID: A unique numeric identifier for the departure aiport OriginAirportName: The full name of the departure airport OriginCity: The departure airport city OriginState: The departure airport state DestAirportID: A unique numeric identifier for the destination aiport DestAirportName: The full name of the destination airport DestCity: The destination airport city DestState: The destination airport state CRSDepTime: The scheduled departure time DepDelay: The number of minutes departure was delayed (flight that left ahead of schedule have a negative value) DelDelay15: A binary indicator that departure was delayed by more than 15 minutes (and therefore considered "late") CRSArrTime: The scheduled arrival time ArrDelay: The number of minutes arrival was delayed (flight that arrived ahead of schedule have a negative value) ArrDelay15: A binary indicator that arrival was delayed by more than 15 minutes (and therefore considered "late") Cancelled: A binary indicator that the flight was cancelled

Acknowledgements

The flight delay and cancellation data was collected and published by the DOT's Bureau of Transportation Statistics.

Monthly and annual Canadian arrivals of one or more nights to the U.S. are provided by Statistics Canada for analysis and reporting. A limited amount of U.S. resident travel to Canada is also reported at a monthly level. Monthly level data are reported by mode of transportation with a 3-4 month lag time. Annual data are made available to Tourism Industries at the end of May and a written report with graphics and spreadsheets is generally available in the late summer. The annual report analyzes travelers by province of origin, season of travel, mode of transportation, etc.

This dataset was collected as part of the AWS Academy's Machine Learning Foundations course. This dataset contains scheduled and actual departure and arrival times reported by certified US air carriers that account for at least 1 percent of domestic scheduled passenger revenues. The data was collected by the U.S. Office of Airline Information, Bureau of Transportation Statistics (BTS). The dataset contains date, time, origin, destination, airline, distance, and delay status of flights for flights between 2013 and 2018.

You work for a travel booking website that wants to improve the customer experience for flights that were delayed. The company wants to create a feature to let customers know if the flight will be delayed because of weather when they book a flight to or from the busiest airports for domestic travel in the US.

You are tasked with solving part of this problem by using machine learning (ML) to identify whether the flight will be delayed because of weather. You have been given access to the a dataset about the on-time performance of domestic flights that were operated by large air carriers. You can use this data to train an ML model to predict if the flight is going to be delayed for the busiest airports.

Dataset for Prediction

The flight_delay_predict file is the combination of 60 datasets from 2013 to 2018, but in a reduced version containing only: - Main airports: ATL, ORD, DFW, DEN, CLT, LAX, IAH, PHX, SFO - Top five airlines: UA, OO, WN, AA, DL - Rows with null values were discarded.

Dataset Features

For more information about features in the dataset, see On-time delay dataset features.

Dataset Attribution

Website: Bureau of Transportation Statistics Dataset Compilation: - Compiled by the Office of Airline Information, Bureau of Transportation Statistics (BTS). - Dataset: Airline On-Time Performance Data. - Access at Data Source.

Data Dictionary

1. Year: Year
2. Quarter: Quarter (1-4)
3. Month: Month
4. DayofMonth: Day of Month
5. DayOfWeek: Day of Week
6. FlightDate: Flight Date (yyyymmdd)
7. Reporting_Airline: Unique Carrier Code. When the same code has been used by multiple carriers, a numeric suffix is used for earlier users, for example, PA, PA(1), PA(2). Use this field for analysis across a range of years.
8. DOT_ID_Reporting_Airline: An identification number assigned by US DOT to identify a unique airline (carrier). A unique airline (carrier) is defined as one holding and reporting under the same DOT certificate regardless of its Code, Name, or holding company/corporation.
9. IATA_CODE_Reporting_Airline: Code assigned by IATA and commonly used to identify a carrier. As the same code may have been assigned to different carriers over time, the code is not always unique. For analysis, use the Unique Carrier Code.
10. Tail_Number: Tail Number
11. Flight_Number_Reporting_Airline: Flight Number
12. OriginAirportID: Origin Airport, Airport ID. An identification number assigned by US DOT to identify a unique airport. Use this field for airport analysis across a range of years because an airport can change its airport code and airport codes can be reused.
13. OriginAirportSeqID: Origin Airport, Airport Sequence ID. An identification number assigned by US DOT to identify a unique airport at a given point of time. Airport attributes, such as airport name or coordinates, may change over time.
14. OriginCityMarketID: Origin Airport, City Market ID. City Market ID is an identification number assigned by US DOT to identify a city market. Use this field to consolidate airports serving the same city market.
15. Origin: Origin Airport
16. OriginCityName: Origin Airport, City Name
17. OriginState: Origin Airport, State Code
18. OriginStateFips: Origin Airport, State Fips
19. OriginStateName: Origin Airport, State Name
20. OriginWac: Origin Airport, World Area Code
21. DestAirportID: Destination Airport, Airport ID. An identification number assigned by US DOT to identify a unique airport. Use this field for airport analysis across a range of years because an airport can change its airport code and airport codes can be reused.
22. DestAirportSeqID: Destination Airport, Airport Sequence ID. An identification number assigned by US DOT to identify a unique airport at a given point of time. Airport attributes, such as airport name or coordinates, may change over time.
23. DestCityMarketID: Destination Airport, City Market ID. City Market ID is an identification number assigned by US DOT to identify a city market. Use this field...

This dataset contains scheduled and actual departure and arrival times reported by certified US air carriers that account for at least 1% of domestic scheduled passenger revenues. The data was collected by the Office of Airline Information, Bureau of Transportation Statistics (BTS). The dataset contains date, time, origin, destination, airline, distance, and delay status of flights for flights between 2016 and 2018 The report, focusing on data from year 2016-2018, estimated that air transportation delays put a 4 billion dollar dent in the country's gross domestic product that year. Full report can be found here. In order to answer this question, we are going to analyze the provided dataset, containing up to 18 M different internal flights in the US for 2016-2018 and their causes for delay, diversion and cancellation; if any. The data comes from the U.S. Department of Transportation's (DOT) Bureau of Transportation Statistics (BTS).

This dataset is composed by the following variables: Number Column Name Description 1 **Year **2016, 2017, 2018 2 **Month **1-12 3 **DayofMonth **1-31 4 **DayOfWeek **1 (Monday) - 7 (Sunday) 5 DepTime actual departure time (local, hhmm) 6 **CRSDepTime **scheduled departure time (local, hhmm) 7 **ArrTime **actual arrival time (local, hhmm) 8 **CRSArrTime **scheduled arrival time (local, hhmm) 9 **ActualElapsedTime **in minutes 10 **CRSElapsedTime **in minutes 11 **AirTime **in minutes 12 **ArrDelay **arrival delay, in minutes: A flight is counted as "on time" if it operated less than 15 minutes later the scheduled time shown in the carriers' Computerized Reservations Systems (CRS). 13 **DepDelay **departure delay, in minutes 14 **Origin **origin IATA airport code 15 **Dest **destination IATA airport code 16 **Distance **in miles 17 **TaxiIn **taxi in time, in minutes 18 **TaxiOut **taxi out time in minutes 19 **Cancelled ***was the flight cancelled 20 **CancellationCode **reason for cancellation (A = carrier, B = weather, C = NAS, D = security) 21 **Diverted **1 = yes, 0 = no 22 **CarrierDelay **in minutes: Carrier delay is within the control of the air carrier. Examples of occurrences that may determine carrier delay are: aircraft cleaning, aircraft damage, awaiting the arrival of connecting passengers or crew, baggage, bird strike, cargo loading, catering, computer, outage-carrier equipment, crew legality (pilot or attendant rest), damage by hazardous goods, engineering inspection, fuelling, handling disabled passengers, late crew, lavatory servicing, maintenance, oversales, potable water servicing, removal of unruly passenger, slow boarding or seating, stowing carry-on baggage, weight and balance delays. 23 **WeatherDelay **in minutes: Weather delay is caused by extreme or hazardous weather conditions that are forecasted or manifest themselves on point of departure, enrouted, or on point of arrival. 24 **NASDelay **in minutes: Delay that is within the control of the National Airspace System (NAS) may include: non-extreme weather conditions, airport operations, heavy traffic volume, air traffic control, etc. 25 **SecurityDelay **in minutes: Security delay is caused by evacuation of a terminal or concourse, re-boarding of aircraft because of security breach, inoperative screening equipment and/or long lines in excess of 29 minutes at screening areas. 26 **LateAircraftDelay **in minutes: Arrival delay at an airport due to the late arrival of the same aircraft at a previous airport. The ripple effect of an earlier delay at downstream airports is referred to as delay propagation.

This dataset was downloaded from the US Department of transport website. This website holds both schedule and actual departure and arrival times. Those events were collected and authenticated by US airline carriers responsible for almost 1% of all domestic scheduled passenger revenues. The office of airline information, bureau of transportation statistics (BTS) collected and summarised the complete details.

URL Source https://www.transportation.gov/aviation

1. Table 1 Flight dataset information The dataset contains other information such as origin airports and destination airports, flight numbers, cancelled and diverted flights, taxi-in time and taxi out time, and time and distance (RITA, 2017).

The data is available in CSV format, separated by comma and spread over the one hundred available attributes from the following:

1. Attributes Name No of Similar Attribute Time- Period 6 Unique carrier: 5 Origin Airport 9 Destination airport 9 Departure performance 9 Diversions and cancellation 3 Summary of flight 6 The delay causes 5 Diverted airport information 45 Table 2 Flight dataset variables information

   Dataset download The original downloaded file which was in CSV format contained one hundred variables. Among the one hundred variables, this study has utilised 28 variables and the rest of the variables were deleted from the data file. This study has downloaded 12 data files which covered the time from July 2016 till July 2017. The downloading process took around 45 minutes and was downloaded in ZIP format. Each Zip file was 216 MB in size and contains 502458 records.

There were one hundred variables in the original file, but this study decided to keep only 28 variables for analysis depending on their importance. A brief description of those variables are as follows:

Field Name Type Description Year Integer Year of the flight Month Integer Month of flight Day Integer Day of the flight DayOfWeek Integer Day of the flight Flight_Date text Date of the flight UniqueCarrier text (This code assigns to each individual airline for analysis) Tail_Num text Tail Number of the flight FlightNum text Flight Number

Origin_Airport text Origin Airport Origin_City_Name text Origin City Name Origin_State text Origin State Scheduled_Departure Integer Scheduled Departure Departure_Time Integer Departure Time Dep_Delay Integer Departure Delay less than 15 minutes DepDel15 Integer Departure Delay more than 15 minutes

Dep_Delay_Groups Integer Departure Delay Groups Arrival_Time Integer Flight Arrival Time Arrival_Delay Integer Flight Arrival Delay Arr_Del_morethan15 Integer Arrival Delay more than 15 minutes Cancelled Integer Flight Cancelled indicator Diverted Integer Flight Diverted indicator Distance Integer Flight Distance DistanceGroup Integer Flight Distance Group Carrier_Delay Integer Carrier Delay WeatherDelay Integer Delay due to Weather NAS_Delay Integer National Air System Delay, in Minutes Security_Delay Integer Security Delay, in Minutes Late_Aircraft_Delay Integer Late Aircraft Delay, in Minutes

The following are new variables added in the table below after performing pre-processing. Field Name Type Description Top_Carriers Integer Top Carrier Indicator Top_Origin Integer Top Origin Indicator DEPTIME_GROUP1 text Departure Time Group 1 DEPTIME_GROUP2 tex t Departure Time Group 2 DEPTIME_GROUP3 text Departure Time Group 3

Hoàn hảo 👍 Dưới đây là bản mô tả (description) hoàn chỉnh — em chỉ cần copy và dán trực tiếp vào phần “Dataset Description” trên Kaggle. Mình đã viết theo chuẩn phong cách Kaggle (ngắn gọn, chuyên nghiệp, có markdown đẹp).

✈️ Flight Delays Dataset (Sample of 10,000 US Flights)

📘 Overview

This dataset contains detailed information on 10,000 domestic flights within the United States during 2014. It was derived from a larger FAA dataset and includes essential flight attributes such as departure and arrival times, delays, carrier codes, origin and destination airports, and distances.

It’s a great dataset for practicing:

• Data cleaning and preprocessing
• Exploratory data analysis (EDA)
• Predictive modeling for flight delay estimation
• Data visualization and dashboard creation

📊 Dataset Structure

📈 Quick Summary

• Rows: 10,000
• Columns: 16
• Time range: Entire year of 2014
• Missing values: Present in dep/arr times, delays, and tail numbers
• Common carriers: Alaska Airlines (AS), Virgin America (VX), Southwest (WN), etc.

💡 Use Cases

• Analyzing flight delay patterns across time, airlines, and routes
• Predicting flight delays using regression or classification models
• Creating interactive dashboards or visualizations of flight traffic
• Integrating with weather or airport datasets for deeper insights

⚙️ Data Source

This dataset is a curated sample inspired by the nycflights13 dataset — a well-known dataset used in many Data Science and Machine Learning tutorials.

📜 License

This dataset is shared for educational and research purposes under the CC BY 4.0 License.

🧠 Keywords

flight delays, aviation, transportation, data analysis, machine learning, EDA, Hadoop, Spark, Big Data

The T-100 Domestic Market and Segment Data dataset was downloaded on April 08, 2025 from the Bureau of Transportation Statistics (BTS) and is part of the U.S. Department of Transportation (USDOT)/Bureau of Transportation Statistics (BTS) National Transportation Atlas Database (NTAD). It shows 2024 statistics for all domestic airports operated by US carriers, and all information are totals for the year across all four (4) service classes (F - Scheduled Passenger/ Cargo Service, G - Scheduled All Cargo Service, L - Non-Scheduled Civilian Passenger/ Cargo Service, and P - Non-Scheduled Civilian All Cargo Service). This dataset is a combination of both T-100 Market and T-100 Segments datasets. The T-100 Market includes enplanement data, and T-100 Segment data includes passengers, arrivals, departures, freight, and mail. Data is by origin airport. Along with yearly aggregate totals for these variables, this dataset also provides more granular information for the passenger and freight variable by service class and by scheduled vs non-scheduled statistics where applicable. A data dictionary, or other source of attribute information, is accessible at https://doi.org/10.21949/1529081

The "flights.csv" dataset contains information about the flights of an airport. This dataset includes information such as departure and arrival time, delays, flight company, flight number, flight origin and destination, flight duration, distance, hour and minute of flight, and exact date and time of flight. This data can be used in management analysis and strategies and provide useful information about the performance of flights and placement companies. The analysis of the data in this dataset can be used as a basis for the following activities: - Analysis of time patterns and trends: by examining the departure and arrival time of the aircraft, changes and time changes, patterns and trends in flight behavior can be identified. - Analysis of American companies: By viewing information about airlines such as the number of flights, the impact and overall performance, you can compare and analyze the performance of each company. - Analysis of delays and service quality: By examining delays and arrival time, I can collect and analyze information about the quality of services provided by the airport and companies. - Analysis of flight routes: by checking the origin and destination of flights, distances and flight duration, popular routes and people's choices can be identified and analyzed. - Analysis of airport performance: by observing the characteristics of flights and airport performance, it is possible to identify and analyze the strengths and weaknesses of the airport and suggest improvements.

It provides various tools for data analysis and visualization and can be used as a basis for managerial decisions in the field of aviation industry.

Airline Company Codes (in order of frequency for this dataset)

WN -- Southwest Airlines Co.

DL -- Delta Air Lines Inc.

AA -- American Airlines Inc.

UA -- United Air Lines Inc.

B6 -- JetBlue Airways

AS -- Alaska Airlines Inc.

NK -- Spirit Air Lines

G4 -- Allegiant Air

F9 -- Frontier Airlines Inc.

HA -- Hawaiian Airlines Inc.

SY -- Sun Country Airlines d/b/a MN Airlines

VX -- Virgin America