The goal of this article is to predict flight prices based on a variety of variables. The data utilized in this post may be found on Kaggle. Because the price is the target or dependent variable, this will be a regression problem (continuous numeric value). The number of people who fly has dramatically increased in recent years. Pricing alters dynamically owing to many variables, making it difficult for airlines to maintain prices.

In this article, we are going to predict the price of a flight considering some features such as Airline, Source, Destination, Route, Date of Journey, Total Stops, and some additional info. Let's start by importing the libraries: It is an approach to analyzing data by summarizing the main characteristics of the data. From the information, the Date_of_Journey feature is an object type. To use this column in the features then we have to convert it into a timestamp for prediction. We need pandas to function to_datetime to convert object data type to timestamp type.