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Flight Ticket Cost Forecast Data
Aerospace & Aviation
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About
This dataset is designed for flight price prediction, framed as a regression problem within a competition format. It offers valuable insights into factors influencing airline ticket costs, making it ideal for developing and testing predictive models. The dataset was originally sourced from an external link and subsequently adapted for competition purposes.
Columns
- flightId: A unique identifier for each flight.
- airline: The name of the airline operating the flight, such as Vistara or Air_India.
- flight: The specific flight number, for instance, UK-772 or UK-860.
- source_city: The city from which the flight departs, including locations like Mumbai and Delhi.
- departure_time: The time segment of the day when the flight takes off, e.g., Morning, Evening.
- stops: The number of layovers or stops during the journey, typically one or zero.
- arrival_time: The time segment of the day when the flight lands, e.g., Night, Evening.
- destination_city: The city where the flight arrives, including locations like Mumbai and Delhi.
- duration: The total travel time of the flight, measured in hours.
- days_left: The number of days remaining until the scheduled flight departure.
Distribution
The dataset is typically provided in a CSV format. A sample file, X_test.csv, has a size of 229.16 kB and contains 10 columns. Most columns feature 3347 valid records, indicating the number of entries available for analysis. Detailed row counts for specific data splits are not explicitly stated, but the overall record count is consistent across features.
Usage
This dataset is perfectly suited for regression problems and machine learning model development aimed at predicting flight prices. It can be utilised for:
- Building predictive models for airline ticket costs.
- Analysing factors that influence flight pricing strategies.
- Developing algorithms for travel planning and budgeting tools.
- Conducting research into dynamic pricing in the aviation industry.
Coverage
The dataset primarily focuses on domestic Indian air travel, with common source and destination cities including Mumbai and Delhi. The time range covered by the
days_left column spans from 1 to 49 days before a flight's departure. Data on departure_time and arrival_time categorises flights by segments of the day (e.g., Morning, Evening, Night). The dataset is expected to be updated annually. Specific demographic details are not provided.License
CC0: Public Domain
Who Can Use It
This dataset is beneficial for:
- Data Scientists and Machine Learning Engineers for developing and evaluating flight price prediction models.
- Travel Analysts and Industry Researchers for understanding market dynamics and pricing trends.
- Students and Academics for educational projects and research in predictive analytics.
- Software Developers building applications that require airfare forecasting.
Dataset Name Suggestions
- Airline Price Prediction Dataset
- Flight Ticket Cost Forecast Data
- Air Travel Price Prediction Challenge
- Indian Flight Price Regression Data
Attributes
Original Data Source: Flight Ticket Cost Forecast Data
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