merekat/flight-delay-prediction

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. ## 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 - The 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 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 …