Content-based airline recommendation prediction using machine learning techniques

Abstract

Presently, We are living in the 21st Century, with a population of around 7.7 Billion people in the world. There is a rapid increase in the number of customers traveling by airplane day by day. The number of airplane companies has also increased considerably, in order to meet the requirement of the customers. When there is a number of companies serving the same purpose, it creates confusion among people, which one is to choose. So it has become very important for the travelers to know, which airline could be the best for them as per their demand and budget. It can be known by the honest reviews submitted by the travelers by sharing their previous experience. The reviews submitted not only help the customers to choose the appropriate airline but also help the airline companies to know their shortcomings and improve their quality of service. Since airline reviews data is online and its huge amount of data, so it becomes difficult to manage things manually. Due to this fact, there is a need for a model that can help in the recommendation. This chapter aims at distinguishing reviews as positive or negative from the content of the online customer reviews submitted by the previous customer and providing a recommendation. In this paper, we have compared three machine-learning algorithms namely Logistic Regression, Stochastic Gradient Descent (SGD) and Random Forest Classifier. It also predicts the accuracy of recommendation done by the ML Techniques.