https://github.com/simonbernarding/ml_project_simonbernarding

This project focuses on predicting flight delays using historical data from a Tunisian airline. We analyzed patterns in airport operations and flight schedules to build a machine learning model that can forecast potential delays.
https://github.com/simonbernarding/ml_project_simonbernarding

data data-science flight-delay-prediction machine-learning machinelearning prediction

Last synced: 9 months ago
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This project focuses on predicting flight delays using historical data from a Tunisian airline. We analyzed patterns in airport operations and flight schedules to build a machine learning model that can forecast potential delays.

• Host: GitHub
• URL: https://github.com/simonbernarding/ml_project_simonbernarding
• Owner: SimonBernarding
• License: mit
• Created: 2024-07-12T12:00:55.000Z (almost 2 years ago)
• Default Branch: simon
• Last Pushed: 2024-07-12T12:18:11.000Z (almost 2 years ago)
• Last Synced: 2025-06-19T07:09:05.682Z (about 1 year ago)
• Topics: data, data-science, flight-delay-prediction, machine-learning, machinelearning, prediction
• Language: Jupyter Notebook
• Homepage:
• Size: 53.8 MB
• Stars: 1
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

          
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## Flight Prediction Test on Airport Data from Tunesian Airline

Based on several machine learning classifier this project tries to predict delays of individual airplanes.

### Set up the Presentation

- Thre presentation can be started with streamlit. Make sure to have streamlit installed in your directory, as described in the requirements. 

     ```BASH

    streamlit run app.py

    ```

    After that a local host is started in your standard browser. 

 

## Set up your Environment

### **`macOS`** type the following commands : 

- For installing the virtual environment you can either use the [Makefile](Makefile) and run `make setup` or install it manually with the following commands:

     ```BASH

    make setup

    ```

    After that active your environment by following commands:

    ```BASH

    source .venv/bin/activate

    ```

Or ....

- Install the virtual environment and the required packages by following commands:

    ```BASH

    pyenv local 3.11.3

    python -m venv .venv

    source .venv/bin/activate

    pip install --upgrade pip

    pip install -r requirements.txt

    ```

    

### **`WindowsOS`** type the following commands :

- Install the virtual environment and the required packages by following commands.

   For `PowerShell` CLI :

    ```PowerShell

    pyenv local 3.11.3

    python -m venv .venv

    .venv\Scripts\Activate.ps1

    pip install --upgrade pip

    pip install -r requirements.txt

    ```

    For `Git-bash` CLI :

  

    ```BASH

    pyenv local 3.11.3

    python -m venv .venv

    source .venv/Scripts/activate

    pip install --upgrade pip

    pip install -r requirements.txt

    ```

    **`Note:`**

    If you encounter an error when trying to run `pip install --upgrade pip`, try using the following command:

    ```Bash

    python.exe -m pip install --upgrade pip

    ```

   

## Usage

In order to train the model and store test data in the data folder and the model in models run:

**`Note`**: Make sure your environment is activated.

```bash

python example_files/train.py  

```

In order to test that predict works on a test set you created run:

```bash

python example_files/predict.py models/linear_regression_model.sav data/X_test.csv data/y_test.csv

```

## Limitations

Development libraries are part of the production environment, normally these would be separate as the production code should be as slim as possible.