Dynamic pricing strategy for airline seat booking using SK learn

Abstract

This project aims to optimize airline seat booking through the implementation of a dynamic pricing strategy using machine learning techniques, specifically decision trees and random forests from the scikit-learn library in Python. The objective is to predict the optimal price for airline seats based on various factors such as demand, time until departure, seasonality, historical booking data, and competitor pricing. By leveraging these machine learning algorithms, the model can dynamically adjust seat prices in real-time to maximize revenue and occupancy rates. Decision trees provide insights into the factors influencing pricing decisions, while random forests enhance prediction accuracy by aggregating multiple decision trees. Through this approach, airlines can adapt their pricing strategies dynamically, leading to improved profitability and customer satisfaction.