Popular GitHub Repositories for Airline

Twitter US Airline数据集情感分析（sentiment Analysis），使用Bert Sentence encoding作为特征，实现了SVM、XGBoost、RandomForest（随机森林）若干分类器。

DeepAir Solutions : Price recommendations for ancillary facilities for airline using deep reinforcement learning. AAP refers to Airline Ancillary Pricing.

Sample Airline AI Assistant created with Spring AI, Angular and Gemini

This project analyzes and visualizes airline delay causes using data sourced from Kaggle. Additionally, it applies machine learning techniques to predict and understand factors contributing to flight delays, such as weather, airline operations, and more.

In this repository I have utilised 6 different NLP Models to predict the sentiments of the user as per the twitter reviews on airline. The dataset is Twitter US Airline Sentiment. The best models each from ML and DL have been deployed. It employs text preprocessing,

Forecast airline passenger demand using time series models like AR, ARMA, and LSTM to improve operations, optimize scheduling, enhance resource allocation, and streamline supply chain management through accurate demand predictions

Using machine learning algorithms like support vector machines, artificial neural networks and random forest to predict airline customers satisfaction

Factors that lead to customer satisfaction for an Airline. Model built using Python and trained using Amazon SageMaker.

♦️ Twitter US Airline Sentiment Analysis ♦️ Applied text preprocessing, NLP, and sentiment classification to analyze positive, neutral, and negative tweets. Created visualizations like sentiment distribution, airline comparisons, and word clouds for key insights. Delivered a cleaned dataset, insightful analysis, and automated PowerPoint report.

MCP Amadeus Flights Server - A Model Context Protocol server that provides comprehensive flight search capabilities to LLMs using the Amadeus API. Features include flight search, airport lookup, airline information, multi-city flights, price optimization, and robust error handling with TypeScript support.

Sentiment detection model using many-to-one LSTMs on airline text reviews and generate contextually relevant text by training on "Alice's Adventures in Wonderland".

? Streamline ticket management with AI that categorizes, prioritizes, and assigns support tickets to the right moderators for efficient resolutions.

Generate Elon Tweets, McDonald's Reviews and Airline Reviews

A MLOps implementation for sentiment analysis of airline tweets. This project demonstrates a ML lifecycle with traditional machine learning (SVM) and modern MLOps practices.

Automated airline ticket refund processing with AI-powered decision making using OpenAI GPT-4o-mini and Camunda Platform

This project aims to perform sentiment analysis on airline reviews using a Transformer-based model and deploying it with Pinecone, a scalable vector database for building real-time applications. Sentiment analysis is a natural language processing task where the sentiment expressed in a piece of text, such as a review, is determined.

Successfully developed a machine learning model to predict Airline Passenger Satisfaction by building an end-to-end MLOps pipeline. It integrates DVC for data versioning, a Dockerfile for containerization, and CI/CD using GitHub Actions for automated deployment.

This Rasa-based Travel Chatbot helps users plan their trips: Flight & Bus Suggestions: Recommends airline or bus company links based on user preferences. Real-time Flight Tracking: Fetches live flight data or specific flight details using APIs. Ideal for providing personalized travel assistance.

Airline Passenger Pulse: calibrated ML to detect at-risk passengers, explain why (SHAP/counterfactuals), and trigger segment-specific actions.

This project analyzes 19M+ flight records (sourced from Kaggle in Parquet format) to uncover trends in delays, taxi times, and cancellations—driving insights for improved airline scheduling and operational efficiency.