Flight Delay Prediction

MIDS W261: Machine Learning at Scale | Fall 2021 | Final Project

Authors: Toby Petty, Zixi Wang, Yao Chen, Ferdous Alam

Introduction

This repo contains my group's final project for the Machine Learning at Scale class (W261) in UC Berkeley's Masters in Data Science program, from the Fall 2021 semester. The problem involves predicting flight delays using a dataset of ~30m flights over a 5 year period, along with a supplementary dataset of more than 700m weather observations.

The project was implemented on a distributed computing cluster on the Databricks cloud platform, using PySpark for all data engineering, modeling, prediction, and evaluation.

Start here

For a full overview see the final report detailing the problem, our approach, and the full technical specifications of the solution pipeline.

Full Contents

• code - executable Python files to run the full pipeline from start to finish:
  • full_data_pipeline.py - data engineering pipeline to clean, transform, and augment the raw data to produce final train/test datasets suitable for supervised machine learning.
  • gridsearch_cv.py - custom class to grid-search parameters for different models in a cross-validation method designed for sequential time-series data.
  • model_selection_ensemble.py - ensemble of the best machine learning models found in the grid-searches to make final predictions and evaluate performance on the test dataset.
• notebooks - Databricks/Jupyter notebooks detailing each stage of the pipeline, along with full EDA, and the final written report.
• Flight Delay Prediction - Presentation Slides.pdf - slide deck for the final presentation of our project work and findings.