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Flight Delay Prediction Project

Overview

This project focuses on predicting flight delays using a robust machine learning pipeline implemented with PySpark. The goal is to provide accurate predictions by optimizing model performance and aligning evaluation metrics with business objectives.

Key Features

  • ML Pipeline: Built and deployed a flight delay prediction model using PySpark.
  • Hyperparameter Tuning: Used Hyperopt for systematic tuning to achieve optimal model performance.
  • Performance Metrics: Achieved an F-beta score of 0.9, balancing precision and recall effectively.

Files

  • Flight-Prediction Phase 3 - (Final Report).ipynb: The final report on the model and its performance.
  • Phase 3 - HyperOpt.ipynb: The notebook demonstrating hyperparameter tuning with Hyperopt.
  • Phase 3 - Modeling - best.ipynb: The notebook containing the best-performing model.
  • Team 6-2 Presentation (Final).pdf: Final presentation slides summarizing project outcomes.

Installation & Setup

  1. Clone this repository.
  2. Install required dependencies via pip install -r requirements.txt.
  3. Run the notebooks to explore data processing, model training, and evaluation.

License

This project is licensed under the MIT License.

Let me know if you'd like any changes!