Prediction of Flight Areas using Machine Learning Algorithm
DOI:
https://doi.org/10.62486/latia202493Keywords:
Machine Learning, Linear Regression, SVM, Decision Tree, KNNAbstract
Anyone who often uses the airways wants to predict when it will be best to purchase a ticket in order to get the best possible value. Aircraft firms continuously adjust ticket prices in an effort to maximize profits. When it's anticipated that demand for more income will grow, aircraft manufacturers may raise flying prices. Information analysis for a given air route, comprising the features like take-off time, entrance time, and airways during a specified period, has been gathered in order to decrease costs. To use the machine learning models, qualities are arranged based on the information that has been gathered. The machine learning approach to determine costs based on attributes is presented in the paper below.
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