ABSTRACT
This chapter contains how data is processed in order to predict prices of things and also how the prediction of prices of airlines tickets is done from scratch. Along with the accuracy in predicting prices of each model, this chapter contains how every accuracy of the derived features of the dataset is dependent on the prediction of prices of different airline tickets and deals with the computation of airline tickets using machine learning algorithms. For this process, a dataset on previously scheduled airline tickets in a time from different airline companies is used. The given data is preprocessed by using data cleaning, data wrangling, and different data science techniques to make the data ready to gain insights. To understand the algorithm or method used for predicting fares of previous airline tickets. To calculate the possible minimum price of the tickets, data for certain air routes with different flights and their details have been collected including the features such as time of departure, time of arrival, and of which airways flight is over a certain a period of time to implement algorithms on it and extract useful features. To obtain different useful features from the data by applying different machine learning algorithms, it includes the machine learning algorithms which are used to predict the prices at the given time with the limitations they have.
