HACKATHON

Fly-High Data Hackathon

Welcome to the "Fly-High Data Hackathon"! In this hackathon, your task is to leverage the given flight price dataset to develop a predictive model that accurately forecasts airline prices.

 


 

Winner's Solutions

- First Place

- Second Place

- Third Place

Hackathon ended.

No. of Participants: 100

Starting From: 5th August 2023, 12:00 AM

Ending At: 9th September 2023, 12:00 AM

Prizes

The prize money will be distributed as follows:

  1. First Place: $600
  2. Second Place: $300
  3. Third Place: $100

This hackathon contains the dataset for flight data. The goal is to predict to the correct flight price based on the variables that you think are the right ones to make the prediction. The model's performance will be evaluated based on its ability to minimize the Root Mean Square Error (RMSE) between the model's predicted values and the actual flight prices.

The dataset is in CSV format, which consists of various features related to flight prices, such as departure city, destination city, date and time of flight, flight duration, airline, etc. Your mission is to extract valuable insights from this data and create a model that can predict flight prices as accurately as possible.

 

 

For any queries you can contact rohan@projectpro.io

Rules

  1. This is an individual event.
  2. The provided flight price dataset must be used to build the model.
  3. You are allowed to use any data preprocessing and machine learning or deep learning techniques to achieve the goal.
  4. The participants must submit both the source code and a CSV file, named 'submission.csv,' containing the predicted values for the flight prices.
  5. The source code must be well-documented and written in Python or R.
  6. The use of external datasets for training is not allowed.
  7. The code must be original and not copied from any source.
  8. Any kind of malpractice will result in immediate disqualification.
  9. The leaderboard's current ranking is determined by the scores, but it may be subject to change based on the Code and Brief report in the future.

 

Submission

Your submission should consist of:

  1. Source code files: This should include any scripts (.py files) and auxiliary files used in your solution.
  2. Submission.csv: This CSV file should contain your model's flight price predictions.
  3. A brief report (max 2 pages in pdf format) describing your approach, techniques used, and rationale for your decisions.

 

Evaluation Criteria

Submissions will be judged based on the RMSE value of the predicted flight prices in the test dataset compared to the actual values. The lower the RMSE, the better. In the case of a tie, the winner will be decided based on the simplicity and efficiency of the code, and the brief report.

 

Deadline

The hackathon starts on August 5, 2023. All submissions must be made by 11:59 PM (IST) on September 8, 2023. Submissions received after the deadline will not be considered for evaluation.

Overview

The data has been split into two groups:

 

The train set should be used to build your machine learning models. For the training set, we provide the outcome (also known as the “Flight_Price ”) for each flight.

 

The test set should be used to see how well your model performs on unseen data. For the test set, we do not provide the fare for each flight. It is your job to predict these outcomes. For each flight in the test set, use the model you trained to predict the price of the flight ticket.

 

We also include submission.csv, as an example of what a submission file should look like.

 

Data Dictionary

Here are the column values and their descriptions:

The columns are as follows: 

  1. Flight_ID: A unique identifier for each flight.
  2. Airline: The airline operating the flight.
  3. Departure_City: The city from which the flight departs.
  4. Arrival_City: The city where the flight arrives.
  5. Distance: The distance between the departure and arrival cities (in kilometers).
  6. Departure_Time: The time of day when the flight departs (in 24-hour format).
  7. Arrival_Time: The time of day when the flight arrives (in 24-hour format).
  8. Duration: The duration of the flight (in hours).
  9. Aircraft_Type: The type of aircraft used for the flight.
  10. Number_of_Stops: The number of stops during the journey.
  11. Day_of_Week: The day of the week when the flight takes place.
  12. Month_of_Travel: The month when the flight takes place.
  13. Holiday_Season: Indication of whether the flight occurs during a holiday or a specific season.
  14. Demand: A metric representing the demand for flights on that specific route.
  15. Weather_Conditions: Weather conditions during the flight.
  16. Passenger_Count: The number of passengers on the flight.
  17. Promotion_Type: The type of promotion offered for the flight. 
  18. Fuel_Price: The cost of fuel (per liter), which can significantly influence ticket prices.
  19. Flight_Price: The price of the flight ticket.
Note - The leaderboard's current ranking is determined by the scores, but it may be subject to change based on the Code and Brief report in the future.

User Rank Score No. of Submissions

papaemman.pan

1

12.1111

4

shouryachouhan

2

12.4109

3

simranjeetsingh1497

3

13.5699

1

naqatiaaqib

4

14.0257

1

justforapps1515

5

14.091

4

diegofranco711

6

15.2248

4

msmerkury

7

16.3752

5

Thiruthirurec

8

16.4724

2

minityn

9

20.5365

3

Melwakil2

10

21.229

18

mailmeatss2112

11

29.5198

3

hiteshmadapathi2

12

30.6204

2

bhargav_annavajhula

13

41.3325

1

sharath16081996

14

262.686

3

Participate in Hackathon to be able to submit the code.