Flight-Price-Prediction-EDA

Performed Exploratory Data Analysis on a flight ticket price dataset to uncover key insights. Analyzed features like duration, stops, departure time, and airline. Identified patterns, handled missing data, and visualized factors influencing ticket prices for better prediction.

FEATURES

The various features of the cleaned dataset are explained below:

1. Airline: The name of the airline company is stored in the airline column. It is a categorical feature having 6 different airlines.
2. Flight: Flight stores information regarding the plane's flight code. It is a categorical feature.
3. Source City: City from which the flight takes off. It is a categorical feature having 6 unique cities.
4. Departure Time: This is a derived categorical feature obtained created by grouping time periods into bins. It stores information about the departure time and have 6 unique time labels.
5. Stops: A categorical feature with 3 distinct values that stores the number of stops between the source and destination cities.
6. Arrival Time: This is a derived categorical feature created by grouping time intervals into bins. It has six distinct time labels and keeps information about the arrival time.
7. Destination City: City where the flight will land. It is a categorical feature having 6 unique cities.
8. Class: A categorical feature that contains information on seat class; it has two distinct values: Business and Economy.
9. Duration: A continuous feature that displays the overall amount of time it takes to travel between cities in hours.
10. Days Left: This is a derived characteristic that is calculated by subtracting the trip date by the booking date.
11. Price: Target variable stores information of the ticket price.