Malisha4065/FlightDelayPredictionGroup99

This project focuses on predicting flight delays in the United States domestic air traffic system over 500 000+ data using machine learning techniques. Leveraging a dataset from the Bureau of Transportation Statistics for the year 2020, we aim to develop a predictive model that can anticipate flight delays with 93.1 % high accuracy.

Flight Delay Prediction using Machine Learning

Overview

This project focuses on predicting flight delays in the United States domestic air traffic system over 500 000+ data using machine learning techniques. Leveraging a dataset from the Bureau of Transportation Statistics for the year 2020, we aim to develop a predictive model that can anticipate flight delays with with (SVM 93.10% and KNN 87.86%) high accuracy.

• Data Set - https://www.kaggle.com/datasets/divyansh22/february-flight-delay-prediction

Usage

1. Clone the repository: https://github.com/Malisha4065/FlightDelayPredictionGroup99.git

2. Install dependencies:

3. Explore the notebooks in the notebooks directory to understand the data preprocessing, model training, and evaluation process.

4. Run the source code files in the src directory to train the machine learning model and make predictions.

Results

• Our preliminary results indicate promising performance in predicting flight delays using the selected machine learning model.

Using KNN

• Accuracy: 0.8786
• Precision: 0.5671
• Recall: 0.7827
• F1 Score: 0.6577

Using SVM

• Accuracy: 0.9310
• Precision: 0.7782
• Recall: 0.7510
• F1 Score: 0.7644

Comparison between Models

• For detailed analysis and visualizations, refer to the notebook and results directory.

Contributing

Contributions to this project are welcome! Feel free to fork the repository, make improvements, and submit pull requests.

Authors

• Dushmin Malisha
• Sahan Lelwala