Flight Delay Prediction App

This is a web-based application that predicts flight arrival delays based on various flight parameters such as departure delay, distance, taxi-out time, and more. The app uses machine learning (Linear Regression) to train a model on uploaded flight data and allows users to input flight information to make predictions on delays.

Features

• Upload Flight Data: Upload a CSV file containing historical flight data.
• Train a Model: Train a Linear Regression model using the uploaded data.
• Make Predictions: Input specific flight information (airline, origin, destination, etc.) to predict the arrival delay.
• Real-time Feedback: View model performance using Root Mean Squared Error (RMSE) and predicted delay in real-time.

Technologies Used

• Dash: The web framework used for creating the interactive dashboard.
• Python: Core programming language used for the app.
• Scikit-learn: Used for implementing the machine learning model (Linear Regression).
• Pandas: For data manipulation and analysis.
• Numpy: For numerical operations and handling large datasets.

Prerequisites

Before running this app, ensure you have the following installed:

• Python 3.x

Getting Started

1. Clone the repository

   git clone https://github.com/Emranbiswas/Flight-Delay-Prediction.git
   

2. Install the required dependencies

   pip install dash dash_core_components dash_html_components scikit-learn pandas numpy
   

3. Run the application

   python flight_delay_predictor.py
   

4. Access the App

   http://127.0.0.1:8050/
   

5. Upload Data

   Here is the dataset link: (airline_dataset_2023.csv)[https://github.com/Emranbiswas/Flight-Delay-Prediction/blob/main/Data/airline_dataset_2023.csv].

6. Train the Model Click the Train Model button to train the linear regression model on the uploaded dataset. The RMSE (Root Mean Squared Error) will be displayed to indicate the performance of the model.

7. Make Predictions Once the model is trained, fill in the flight details in the input fields (airline, origin, destination, etc.), and click Predict Delay to get the predicted arrival delay for the flight.

Future Improvements

• Add more advanced machine learning models such as Random Forest or XGBoost for better predictions.
• Allow for real-time data integration from flight APIs.
• Provide visualizations for better data analysis (e.g., delay distributions).

License

• This project is licensed under the MIT License. See the LICENSE file for details.

Contact

• For any questions or support, feel free to contact me at [emran.hstu1999@gmail.com].