An India Based Airline Recommendation System Using Sentiment Analysis and Machine Learning Techniques

The Fast Growth of the Aviation Industry in India, this has led to more travel alternatives, making airline choice a complex one for passengers. Passengers have to take several factors into account such as ticket price, journey time, and overall service quality while deciding upon an airline. Currently, there are many airlines Flight booking websites mainly concentrate on integer filters such as cost and stops, often failing to incorporate qualitative areas like customer satisfaction with passengers, among others perception. Further, this proposed Airline Customer Offer Recommendation system using Machine Learning and applying Sentiment Analysis to assist in passenger decision making informed travel decisions. The system combines "structured flight data with unstructured customer" reviews to develop sentiment-based airline recommendations. Natural Language Processing techniques applied to retrieve sentiment polarity from passenger reviews, which is then aggregated at the airline level to show the level of service provided. Many machine learning classifiers, including Logistic Regression, Support Vector Machine, Random Forest, Naive Bayes, XGBoost algorithms are implemented and evaluated. To improve robustness, ensemble models are also developed. Experimental results demonstrate that incorporating sentiment-based features significantly enhances recommendation accuracy and reliability. The proposed system provides balanced, data-driven, and user-centric airline suggestions, improving overall passenger decision confidence.