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Metro City Flight Booking Dataset
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About
This dataset contains information related to flight bookings, collected from the "Ease My Trip" website. Its primary purpose is to enable the prediction of flight prices through analytical study and machine learning techniques [1]. Researchers and data enthusiasts can leverage this data to practise feature engineering and implement ensemble models, ultimately aiming to uncover valuable insights for potential passengers regarding flight ticket pricing [1]. The study focuses on understanding how factors such as airlines, booking lead time, departure and arrival times, and class of travel influence ticket prices [2].
Columns
- Airline: The name of the airline company, a categorical feature with six distinct airlines [3].
- Flight: Information regarding the plane's flight code, a categorical feature [4].
- Source City: The city from which the flight departs, a categorical feature with six unique cities [4].
- Departure Time: A derived categorical feature, grouping time periods into six unique time labels for departure [4].
- Stops: The number of stops between the source and destination cities, a categorical feature with three distinct values [4].
- Arrival Time: A derived categorical feature, grouping time intervals into six distinct time labels for arrival [5].
- Destination City: The city where the flight will land, a categorical feature with six unique cities [5].
- Class: Information on the seat class, a categorical feature with two distinct values: Business and Economy [5].
- Duration: The overall amount of time it takes to travel between cities, a continuous feature expressed in hours [5].
- Days Left: A derived characteristic calculated by subtracting the trip date from the booking date [6].
- Price: The target variable, storing information about the ticket price [6].
Distribution
The dataset is typically provided as a CSV file and is available as 'Clean_Dataset.csv' with a size of 24.68 MB [6, 7]. It comprises 300,261 distinct flight booking options or datapoints, featuring 11 cleaned attributes [2, 3]. The data was collected over a period of 50 days, from 11th February to 31st March 2022 [2].
Usage
This dataset is ideal for:
- Predicting flight prices using statistical algorithms like Linear Regression [1].
- Conducting statistical hypothesis tests to extract meaningful information from flight booking data [1].
- Practising feature engineering and implementing ensemble models for advanced machine learning projects [1].
- Analysing how price varies with airlines [2].
- Investigating the impact of buying tickets just 1 or 2 days before departure on price [2].
- Studying how ticket prices change based on departure and arrival times [2].
- Examining price changes with variations in source and destination cities [2].
- Comparing ticket price differences between Economy and Business class [2].
- Developing an end-to-end project in data analysis and prediction [6].
Coverage
The dataset covers flight travel between India's top 6 metro cities [3]. The temporal scope of the data collection is 50 days, specifically from 11th February to 31st March 2022 [2]. It includes data for both Economy and Business class tickets [2, 5].
License
CC0: Public Domain [6].
Who Can Use It
This dataset is suitable for:
- Data scientists and analysts interested in predictive modelling and statistical analysis [1].
- Machine learning practitioners looking to explore feature engineering, regression techniques, and ensemble modelling [1].
- Researchers focusing on transportation economics, pricing strategies, or consumer behaviour in the aviation sector [1, 2].
- Students and beginners in data science and analytics, as it is tagged for both Beginner and Intermediate users [6].
Dataset Name Suggestions
- Indian Flight Price Prediction Data
- EaseMyTrip Flight Analytics
- Airline Ticket Pricing Study
- Metro City Flight Booking Dataset
- Flight Price Dynamics 2022
Attributes
Original Data Source: Metro City Flight Booking Dataset
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