Popular GitHub Repositories for Airport Passengers

[IEEE TKDE | TITS 2023] "Learning Large Neighborhood Search for Vehicle Routing in Airport Ground Handling" | "Neural Airport Ground Handling"

Our machine learning project focuses on building and evaluating predictive models for cab fare prediction. We perform extensive data processing, cleaning, and feature extraction to prepare the dataset for model training. This project aims to predict cab fares accurately based on various input parameters such as location, no. of passengers and time.

Development of a lane detection and tracking solution for self-driving cars. This is an important part of the autopilot system, to ensure the safety and efficiency of the driver and passengers on the road.

Anomaly detected using aerial imagery can be of many types like unknown ship in docks, or a unidentified aircraft at the airport and other similar anomalies. In this project we've mainly focused on ship dataset to detect all the ships in a waterbody using keras-retinanet.

Training an Autonomous Vehicle using Reinforcement Learning. Implements an optimized Q-Learning driving agent that that navigates a Smartcab through its environment towards a goal. The Smartcab agent simulates transporting passengers from one location to another, evaluating two very important metrics: Safety and Reliability.

Imagine boarding the Titanic in 2021, and you have provided all your details as a passenger to the captain. There is are three people involved, the data scientist, captain and the passenger. Imagine the company who has built Titanic has created a machine ML model to predict the rate of survival of the passengers, in case of a disaster. The job of the data scientist is to make a model that is explainable to the passengers who are not technical, and that they get the answer about the reasons why they may not survive.

An AI-powered web app to that uses pre-trained deep learning models, in order to improve passengers safety and railway operations.

This repository showcases an implementation of a chat application leveraging the power of Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG). By integrating AlloyDB and Vertex AI, the application delivers precise, contextually relevant responses to flight and airport inquiries.

This project aims to predict the number of passengers, freight quantity, and mail quantity for American airlines operating between Canadian and U.S. airports using an MLOps approach. It involves automating the data pipeline, from data extraction and preparation to model training and evaluation, leveraging tools like DVC, MLflow, and Docker for vers

?? A smart WhatsApp chatbot built with Flask, Twilio, and Hugging Face to help users find the cheapest flights using natural language. Real-time flight data powered by AviationStack and intelligent airport detection included. Perfect for travel booking automation and AI chatbot enthusiasts.

A CoreML powered iOS app that detects the scene of an image from 205 categories such as an airport terminal, bedroom, forest, coast, and more.

A straightforward application of facebook's prophet package by using it to predict airport passenger numbers through Schiphol airport.

Homelander Level X-Ray Vision for Security – See Everything, Miss Nothing ?✈️

AI app for Airport Code Extractor

一个强大的每日交通客流量预测系统，它结合了经典的时间序列模型和深度学习，以实现更高的准确性和可靠性。

GCP appEngine deployment code of a airport complaint raising bot by collecting required datas from users

A machine learning project to predict whether passengers were transported to an alternate dimension in the Spaceship Titanic dataset. It involves data cleaning, model training with LGBM and XGBoost, and model performance comparison.

This research investigates flight delay trends, examining departure time, airline, and airport factors. Regression machine learning meth- ods are utilized to predict delay contributions from various sources. Time-series models, including LSTM, Hybrid LSTM, and Bi-LSTM, are compared with baseline regression models such as Multiple Regression, Decisi