Vehicle Tracking and Prediction System

Introduction: This project is a component of a larger framework aimed at developing a vehicle tracking and prediction system. The primary objective of this Android application, built with Jetpack Compose, is to serve as a data collection tool for training machine learning models and making predictions based on the collected data.

Features:

1. Data Visualization (Screen 1): The app fetches sample data from a Google Sheet and displays the available data fields to the user.
2. Data Input (Screen 2): Users can manually input data with corresponding labels, including sensor values, directly into the Google Sheet.
3. Model Accuracy (Screen 4): The app displays the current accuracy of the trained machine learning model.
4. Prediction (Screen 5): Based on the trained model, the app can make predictions using the inputted data.

Machine Learning Integration: This project is integrated with a machine learning component, which allows for training models and making predictions based on the collected data. The machine learning aspect of the project is handled separately, but the data collected through this application serves as input for training and prediction purposes.

Getting Started:

To get started with the project, follow these steps:

1. Clone the repository:

   • Clone the project repository to your local machine using the following command:

     git clone [<repository_url>](https://github.com/alive2002yb/Vehicle_Tracking_Input.git)
     

2. Set Up Google Sheet:

   • Create a Google Sheet to store the collected data.
   • Ensure the Google Sheet is accessible to the application for reading and writing data.

3. Build and Run the Application:

   • Open the project in Android Studio.
   • Build and run the application on an Android device or emulator.

4. Explore Screens:

   • Navigate through the screens to visualize data, input new data, view model accuracy, and make predictions.

5. Integrate with Machine Learning Component:

   • Ensure the machine learning component is set up and configured to use the collected data for training and prediction.

Contributing: Contributions to this project are welcome. If you have any ideas for improvements or new features, feel free to fork the repository and submit a pull request.

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

Acknowledgements: We would like to thank the contributors and the open-source community for their valuable contributions and support towards this project.

Contacts: For any inquiries or support, please contact ybakhru2002@gmail.com.

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