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Predict The Flight Ticket Price Hackathon Logo

PredictTheFlight
TicketPriceHackathon

Expired
Start: March 6, 2019Ends: November 2, 2025
Participants
4,243
Time Left
Ended
Subs/day
10
Challenge Overview

Flight ticket prices are often hard to predict. One day, you might see a price, and by the next day, it’s an entirely different story. Travelers frequently lament how unpredictable flight ticket prices can be. Well, it’s time to change that!

As data scientists, we embrace the challenge: with the right data, anything can be predicted. In this task, you will analyze flight ticket prices for various airlines between March and June of 2019, covering flights between multiple cities.

Dataset Overview:

  • Training Set Size: 10,683 records
  • Test Set Size: 2,671 records

Features:

  1. Airline: The name of the airline.
  2. Date_of_Journey: The date of the journey.
  3. Source: The origin of the flight.
  4. Destination: The final destination of the flight.
  5. Route: The route taken by the flight to reach the destination.
  6. Dep_Time: The departure time from the source.
  7. Arrival_Time: The time of arrival at the destination.
  8. Duration: The total duration of the flight.
  9. Total_Stops: The number of stops between the source and destination.
  10. Additional_Info: Any extra information about the flight.
  11. Price: The ticket price (target variable).

Your challenge is to use this data to build models that can predict flight ticket prices accurately.

Evaluation Metric:

Problem Statement

This challenge focuses on building advanced machine learning models to solve real-world problems. Participants will work with carefully curated datasets and compete to achieve the best performance metrics.

Target Column: Price
Metric: custom_root_mean_squared_1
Level: Beginner
Submissions: 10/day
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