https://github.com/gaurav0502/flight-delay-prediction
Multiclass Classification of flight delays
https://github.com/gaurav0502/flight-delay-prediction
applied-machine-learning aviation-data python seaborn sklearn
Last synced: 12 months ago
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Multiclass Classification of flight delays
- Host: GitHub
- URL: https://github.com/gaurav0502/flight-delay-prediction
- Owner: Gaurav0502
- Created: 2024-10-26T00:32:30.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2024-12-08T03:13:30.000Z (over 1 year ago)
- Last Synced: 2025-02-05T14:42:44.473Z (over 1 year ago)
- Topics: applied-machine-learning, aviation-data, python, seaborn, sklearn
- Language: Jupyter Notebook
- Homepage:
- Size: 7.66 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Multiclass classification of arrival delay of flights
## Aim
To explore tree-based models for the prediction of the arrival delay of aircrafts on US domestic routes by binning the arrival delay.
## Datasets
- The Airline Delay Dataset is available here.
## Models Used
The following tree-based models are used from `sklearn`:
- Decision Tree
- Random Forest Classifier
- Gradient Boosting Classifier
- Histogram Gradient Boosting Classifier
## Directory structure
```bash
.
├── DelayData.csv
├── README.md
├── main.ipynb
└── requirements.txt
1 directory, 4 files
```
## Instructions to execute the code
The `main.ipynb` notebook can be used after completing the following steps:
- Download the dataset from the URL above.
- Ensure the dataset is named as `DelayData.csv` and in the location same as that of the notebook (refer the directory structure above).
- Install all the required modules from `requirements.txt`.
```bash
pip install -r requirements.txt
```