Vueling Tech Hack

This repository contains the solution to the VUELING TECH HACK.
My name is Guillermo Seoane and I'm a #DataScience student at the IT Academy de Barcelona Activa.

Dashboard-Data Science Challenge

✈️ Context:

At the last Annual General Assembly of IATA, the zero net CO₂ emissions in 2050 (aviation sector) resolution finally got approved. That lets us be one step closer to the Paris Agreement of 2015, accomplishing not exceeding 1.5 ºC the Earth's temperature.

📚 Archives:

In the repository you can find:

• Exploratory_analysis of the data,that allows you to understand it better and find insights (ex: flights per month, most used routes, etc.) based on "Mapping Destination Countries". Download 'Mapping Destination Countries'
• Model: model used for prediction.
• A presentation.pdf explaining how I have reach the solution

🦾 Dataset Dictionary:

• Date: Flight date.
• Origin_Country : Country of origin.
• Origin_Continent : Continent of origin.
• Destination_Country: Country of destination. Target variable.
• Destination_Continent: Destination continent.
• Total_flights: Total number of flights.
• Total_seats: Total number of seats.
• Total_ASKs: (Available Seat Kilometer). Total seat numbers available by the total number of km these seats have flown.

The data has been split into two groups:

• The training set has been used to build the machine learning models. Download 'train.csv'
• The test set has been used to see how well our model performs on unseen data. For the test set, the Destination Country variable is not provided. Download 'test.csv'

📏 Evaluation:

The metric used to evaluate the predictive algorithm has been F1 Score Macro.

• Model: RandomForestClassifier

✅ Submission:

• F1-Score: 0.97
• Download 'JSON'

Date: 08/02/2023

Ranking: 1/4

Score: 1169 / 1200