Predicting the duration of arrival delays for commercial flights.

Overview

Lifecycle

Flight Delay Prediction

Our objective is to predict arrival delays of commercial flights. According to the US Department of Transportation, about 21% of commercial flights scheduled between June 2003 and October 2021 have experienced some form of delay. It is critical for airlines to estimate flight delays as accurately as possible in order to improve customer satisfaction and optimize the income of airline agencies. This project will be evaluated on the basis of arrival delay prediction accuracy for flights

Contributors

  • Jordan Silke GitHub
  • Jonas Bacareza GitHub

Understanding the problem


In an effort to understand some common causes of commercial flight delays, a number of sources were consulted including government agencies and flight-focused blog posts. A brief overview of findings can be found in the Research directory. These common causes will inform feature selection and engineering decisions.

Data description


Data was sourced from a LHL PostgreSQL database and descriptions were provided for each table. We used a custom script to extract the feature names from these description files and the raw data can be found here. The rationale behind missing value processing can be reviewed and reproduced by reading and executing the data_overview notebook. The data from the flights table included in this repository is a randomly sampled subset of the source table.

Recommended exploration


Task Status
Test the hypothesis that the arrival delay is from Normal distribution and that mean of the delay is 0. Be careful about the outliers. ✅
Is average/median monthly delay different during the year? If so, which months have the biggest delays and what could be the reason? ✅
Does the weather affect the delay? 🧰
How are taxi times changing during the day? Does higher traffic lead to longer taxi times? ✅
What is the average percentage of delays that exist prior to departure (i.e. are arrival delays caused by departure delays)? Are airlines able to lower the delay during the flights? ✅
How many states cover 50% of US air traffic? ✅
Test the hypothesis that planes fly faster when there is a departure delay. ✅
When (which hour) do most 'LONG', 'SHORT', 'MEDIUM' haul flights take off? ðŸ”ģ
Find the top 10 the bussiest airports. Does the greatest number of flights mean that the majority of passengers went through a given airport? How much traffic do these 10 airports cover? ðŸ”ģ
Do bigger delays lead to bigger fuel consumption per passenger? ðŸ”ģ

ðŸ”ģ - To do.
✅ - Core task 'complete' (at least a first pass).
🧰 - Work in progress.

Exploration task results can be found here

Owner
Jordan Silke
Jordan Silke
Trading Strategies for Freqtrade

Freqtrade Strategies Strategies for Freqtrade, developed primarily in a partnership between @werkkrew and @JimmyNixx from the Freqtrade Discord. Use t

Bryan Chain 242 Jan 07, 2023
Multi-modal Vision Transformers Excel at Class-agnostic Object Detection

Multi-modal Vision Transformers Excel at Class-agnostic Object Detection

Muhammad Maaz 206 Jan 04, 2023
Labelbox is the fastest way to annotate data to build and ship artificial intelligence applications

Labelbox Labelbox is the fastest way to annotate data to build and ship artificial intelligence applications. Use this github repository to help you s

labelbox 1.7k Dec 29, 2022
Transformer - Transformer in PyTorch

Transformer åŪŒæˆčŋ›åšĶ Embeddings and PositionalEncoding with example. MultiHeadAttent

Tianyang Li 1 Jan 06, 2022
Pretrained models for Jax/Flax: StyleGAN2, GPT2, VGG, ResNet.

Pretrained models for Jax/Flax: StyleGAN2, GPT2, VGG, ResNet.

Matthias Wright 169 Dec 26, 2022
IEEE Winter Conference on Applications of Computer Vision 2022 Accepted

SSKT(Accepted WACV2022) Concept map Dataset Image dataset CIFAR10 (torchvision) CIFAR100 (torchvision) STL10 (torchvision) Pascal VOC (torchvision) Im

1 Nov 17, 2022
Apache Spark - A unified analytics engine for large-scale data processing

Apache Spark Spark is a unified analytics engine for large-scale data processing. It provides high-level APIs in Scala, Java, Python, and R, and an op

The Apache Software Foundation 34.7k Jan 04, 2023
Source code for Task-Aware Variational Adversarial Active Learning

Contrastive Coding for Active Learning under Class Distribution Mismatch Official PyTorch implementation of ["Contrastive Coding for Active Learning u

27 Nov 23, 2022
Usable Implementation of "Bootstrap Your Own Latent" self-supervised learning, from Deepmind, in Pytorch

Bootstrap Your Own Latent (BYOL), in Pytorch Practical implementation of an astoundingly simple method for self-supervised learning that achieves a ne

Phil Wang 1.4k Dec 29, 2022
Implementation of Pix2Seq in PyTorch

pix2seq-pytorch Implementation of Pix2Seq paper Different from the paper image input size 1280 bin size 1280 LambdaLR scheduler used instead of Linear

Tony Shin 9 Dec 15, 2022
A modular application for performing anomaly detection in networks

Deep-Learning-Models-for-Network-Annomaly-Detection The modular app consists for mainly three annomaly detection algorithms. The system supports model

Shivam Patel 1 Dec 09, 2021
Proximal Backpropagation - a neural network training algorithm that takes implicit instead of explicit gradient steps

Proximal Backpropagation Proximal Backpropagation (ProxProp) is a neural network training algorithm that takes implicit instead of explicit gradient s

Thomas Frerix 40 Dec 17, 2022
Official Pytorch Implementation of Length-Adaptive Transformer (ACL 2021)

Length-Adaptive Transformer This is the official Pytorch implementation of Length-Adaptive Transformer. For detailed information about the method, ple

Clova AI Research 93 Dec 28, 2022
codes for paper Combining Dynamic Local Context Focus and Dependency Cluster Attention for Aspect-level sentiment classification

DLCF-DCA codes for paper Combining Dynamic Local Context Focus and Dependency Cluster Attention for Aspect-level sentiment classification. submitted t

15 Aug 30, 2022
An AutoML Library made with Optuna and PyTorch Lightning

An AutoML Library made with Optuna and PyTorch Lightning Installation Recommended pip install -U gradsflow From source pip install git+https://github.

GradsFlow 294 Dec 17, 2022
A TensorFlow implementation of SOFA, the Simulator for OFfline LeArning and evaluation.

SOFA This repository is the implementation of SOFA, the Simulator for OFfline leArning and evaluation. Keeping Dataset Biases out of the Simulation: A

22 Nov 23, 2022
Implementation of Barlow Twins paper

barlowtwins PyTorch Implementation of Barlow Twins paper: Barlow Twins: Self-Supervised Learning via Redundancy Reduction This is currently a work in

IgorSusmelj 86 Dec 20, 2022
A simple baseline for 3d human pose estimation in PyTorch.

3d_pose_baseline_pytorch A PyTorch implementation of a simple baseline for 3d human pose estimation. You can check the original Tensorflow implementat

weigq 312 Jan 06, 2023
Hyperparameter tuning for humans

KerasTuner KerasTuner is an easy-to-use, scalable hyperparameter optimization framework that solves the pain points of hyperparameter search. Easily c

Keras 2.6k Dec 27, 2022
A vanilla 3D face modeling on pose-invariant and multi-lightning image data

3D-Face-Modeling A vanilla 3D face modeling on pose-invariant and multi-lightning image data Table of Contents Background Install Usage Contributing B

Haochen Zhang 1 Mar 12, 2022