A two-stage predictive machine learning engine that forecasts the on-time performance of flights for 15 different airports in the USA based on data collected in 2016 and 2017.

Flight delays prediction and analysis: Machine Learning Approach

BJTU, trainning course, Predicting flight delays with weather data.

A machine learning at scale demo on flight delay prediction. The project includes an exploration of a series of data transformation and ML pipelines in Apache Spark (via Databricks).

Explore predictive modeling with this repository, featuring regression-based models applied to a comprehensive dataset on flight delays and cancellations. Gain insights into factors influencing air travel disruptions and leverage regression techniques to enhance predictions.

This project focuses on predicting flight delays using historical data from a Tunisian airline. We analyzed patterns in airport operations and flight schedules to build a machine learning model that can forecast potential delays.

Predicts flight arrival delays using operational flight features and a Random Forest model. Includes a Streamlit web application for interactive predictions.

Real-time flight delay prediction system featuring XGBoost models, SHAP Explainable AI (XAI), Redis caching, Flask-SocketIO WebSockets, dynamic database fallbacks (PostgreSQL/SQLite), and MLOps data drift monitoring. Built with Flask, React, and Dash.

Two stage model to predict if a flight is delayed and if so, the amount of delay in minutes. Trained using data from 18,00,000 flights of which ~ 30% were delayed

Predicts flight delays in Brazil using ANAC data and machine learning techniques, including exploratory data analysis and model evaluation.

Python project hosted in Google Colab that provides data visualizations & an interactive interface for flight delay prediction leveraging random forest

Weather-aware ML pipeline predicting flight departure delays via binary classification, multi-class classification & regression — raw .docx/.xlsx data cleaned from scratch, Random Forest + Ridge Regression, Kaggle-submitted.

How can we better estimate flight delays?

Attention-based deep neural network for predicting flight delays using aviation data, focusing on feature importance and model accuracy.

This repository is a project that dealing with the problem of Flight scheduling

Exploring supervised learning models on flight delay predictions.

A machine learning-based flight delay prediction system that forecasts arrival delays and classifies flights as delayed or on-time based on various factors like NAS delays, departure delays etc. The project employs Linear Regression for delay prediction, Decision Tree for classification, and data visualization techniques for analysis

Classification & regression on 4.1M US flights (2022) to predict arrival delays — scikit-learn + Power BI

✈️ FlightWise AI is an end-to-end Machine Learning project that predicts flight delays using XGBoost and aviation analytics. Features advanced feature engineering, model comparison, interactive Streamlit dashboards, delay risk prediction, and operational recovery recommendations.

Predicts flight delays using historical airline data — classifies whether a flight will be delayed and estimates delay duration, then uses SHAP and a custom Operational Adjustability Index (OAI) to highlight which delay causes airlines can actually control.