ARC - Enter the Travel-Verse Dataset

Overview

The ARC - Enter the Travel-Verse dataset provides extensive airline ticketing data, enabling the identification of trends and predictive analytics within the travel and tourism industry. The dataset is sourced from ARC, the world's largest repository of airline ticketing information.

About the Dataset

Source: HackerEarth Travel-Verse Challenge
Purpose: To identify trends in airline ticketing data and develop predictive models for B2B and B2B2C applications.
Scope: Encompasses airline transactions, ticketing details, travel segments, and passenger journey information.

Challenge Objective

Participants are required to:

Analyze the provided airline ticketing dataset.
Identify a problem in the travel and tourism industry where predictive analytics can provide actionable insights.
Determine a target audience in the B2B or B2B2C space that benefits from the solution.
Develop a machine learning, data science, or analytics-based solution (e.g., recommender systems, predictive models, dashboards, APIs, or applications).
Demonstrate the business value of the proposed solution through a prototype or visual aid.

Dataset Features

The dataset includes the following fields:

Field	Description
Transaction Key	Unique identifier grouping all flight segments associated with a single transaction.
Ticketing Airline	Airline that issued the ticket.
Ticketing Airline Code	Three-digit airline code used for accounting and revenue management.
Agency	Unique numeric code assigned to a travel agency or corporate travel department (CTD). Blank for direct airline tickets.
Issue Date	Date when the ticket was issued.
Country	Code identifying the country where the ticket was issued.
Transaction Type	Type of transaction: `E` (Exchange), `I` (Issued Ticket), `R` (Refund).
Trip Type	Type of itinerary: `OW` (One-Way), `RT` (Round-Trip), `XX` (Complex).
Segment Number	Each flight segment representing part of a full itinerary.
Marketing Airline	Airline operating the flight segment. Ground travel is represented by `V`.
Flight Number	Number assigned to the flight segment.
Cabin	Cabin class: `Prem` (Business/First Class), `Econ` (Economy).
Origin	Three-character airport code for the origin of the flight.
Destination	Three-character airport code for the destination of the flight.
Departure Date	Scheduled departure date of the flight.

Potential Use Cases

Trend Analysis: Identify fluctuations in airline ticket sales and travel demand over time.
Predictive Analytics: Forecast future travel trends based on historical booking patterns.
Pricing Insights: Analyze fare structures across airlines and predict optimal ticket pricing strategies.
Consumer Recommendations: Build recommender systems for personalized travel itineraries and ticket pricing alerts.
Market Insights for Airlines & Agencies: Provide data-driven insights for airlines and travel agencies to optimize ticket sales and improve customer experience.

How to Use

Download the dataset from the official competition or repository.
Explore the data using Python, R, SQL, or visualization tools like Tableau.
Apply data preprocessing techniques to clean and structure the data.
Develop and train models using machine learning or statistical techniques.
Visualize insights using dashboards, charts, or web applications.

License & Citation

Ensure compliance with dataset usage policies as per the HackerEarth competition guidelines.
If using this dataset, credit ARC and the dataset organizers appropriately.

Contact & Contributions

For discussions, collaborations, or dataset inquiries:

HackerEarth Challenge Page: Enter the Travel-Verse
Community Forums & Discussions: Participate in travel and airline analytics research forums.

Empowering travel insights through data-driven solutions.