Skip to content

AduraX/Flight-Delay-Prediction

Repository files navigation

Flight Delay Prediction

Adura ABIONA, PhD (UNSW)

8 March, 2017

Introduction

This work is based on the Final Challenge of DAT203.2x Principles of Machine Learning course in Microsoft Professional Program in Data Science(MPP-DS). In the challenge, we were requested to predict whether a scheduled passenger flight is delayed or not using a binary-classifier.

However, in this project, I will be using regressor of gradient boosted decision trees to predict flight delay time in minutes (which can be negative). In particular, I will use XGBoost. It is an algorithm that has recently been dominating applied machine learning and Kaggle competitions for structured data.

About

Flight Delay Prediction - ML Project

Resources

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published