Flight ticket prices are difficult to guess; today we may see a price, but check out the price of the same flight tomorrow, it will be a different story. MachineHack’s latest hackathon gives data science enthusiasts, especially who are starting their data science journey, a chance to learn by trying to predict the prices for flight tickets.
In this article, we will learn to prepare the data and build your first machine learning model with a simple approach to solving the Predict Flight Ticket Price Hackathon. We will use Support Vector Regression to predict the flight ticket prices for the given test set.
About the Data Set
This hackathon is about predicting the ever-varying prices of tickets. The dataset consists of data collected from various sources and includes the following features.
- Airline: The name of the airline
- Date_of_Journey: The date of the journey
- Source: The source from which the service begins.
- Destination: The destination where the service ends.
- Route: The route taken by the flight to reach the destination.
- Dep_Time: The time when the journey starts from the source.
- Arrival_Time: Time of arrival at the destination.
- Duration: Total duration of the flight.
- Total_Stops: Total stops between the source and destination.
- Additional_Info: Additional information about the flight.
- Price: The price of the ticket.
The training set consists of 10,683 records and the test set consists of 2,671 records.
Solving Predict Flight Ticket Price Hackathon
You can also use the following links to our top tutorials to help you with this challenge:
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- Getting started with Non-Linear Regression Models in R
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