https://github.com/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.
https://github.com/malisha4065/flightdelaypredictiongroup99

k-nearest-neighbors machine-learning python scikit-learn support-vector-machine

Last synced: 6 months ago
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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.

• Host: GitHub
• URL: https://github.com/malisha4065/flightdelaypredictiongroup99
• Owner: Malisha4065
• Created: 2024-01-15T17:27:51.000Z (over 1 year ago)
• Default Branch: main
• Last Pushed: 2024-03-27T18:35:13.000Z (over 1 year ago)
• Last Synced: 2024-03-27T19:36:39.430Z (over 1 year ago)
• Topics: k-nearest-neighbors, machine-learning, python, scikit-learn, support-vector-machine
• Language: Jupyter Notebook
• Homepage:
• Size: 7.77 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

          
# 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

![KNN_results](./images/KNNConfutionMatrtix.png) 

- Accuracy: 0.8786

- Precision: 0.5671

- Recall: 0.7827

- F1 Score: 0.6577

### Using SVM

![SVM_Results](./images/SVMConfusionMatrix.png)

- Accuracy: 0.9310

- Precision: 0.7782

- Recall: 0.7510

- F1 Score: 0.7644

### Comparison between Models

![Comparison](./images/comparison.png)

- 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