Prediction of Metal Coating Properties for Greener Air Travel

The report for this study can be found here.

User Manual

Cloning of this Repository on Github

• Using the Command Line Tool in your desired IDE, run:

    git clone https://github.com/liewyihseng/20090325_submission.git
  

• This will allow the latest version of source code to be cloned into the workspace.

• If you are facing any issue on cloning this file, do drop me an email at liewyihseng177@gmail.com as this repository is currently still in private mode.

Importing of Anaconda Environment

• Have Anaconda Navigator opened in your machine.
• Head to the Environments tab on the most left part of the window.
• Search for 'Import' that lies at the bottom left part of the window.
• At the 'Local Drive', simply insert the path that directs to environment.yml file within the cloned repository.
• Simply assign a name for the new environment and remember to check the 'Overwrite existing enironment' checkbox.
• After that, simply select 'Import'.
• The process of importing the environment might take awhile, please be patient.
• If you have followed the steps, the designated environment with the environment name you have specified has been imported.

Prerequisites

Installation of Git

• Go to this link: https://git-scm.com/download/win.
• Select the version based on your machine's information.
• Extract the files followed by running of the installer.

Installation of Anaconda

Installation of Anaconda can be accessible through this link : https://docs.anaconda.com/anaconda/install/windows/

Running of Source Code

• After having all the prerequisites done, you are now ready to run the cloned source code.
• Go to Anaconda Navigator and head to the environment tab on the most left part of the window.
• Search for the environment you have imported and click onto the start icon beside the enviroment to boot up the environment.
• Simply head to Home tab and search for Jupyter Notebook.
• Select 'Launch' to have Jupyter Notebook booted up.
• Within Jupyter Notebook, head to directory containing the repository.
• Click on the files you would like to access.
• To run the Machine Learning technique training, click on the run all symbol in the navigation bar.
• The training will automatically start where a series of output will be presented.

Python Version

The project within this repository utilises Python 3.7.11

Attribute

All files included inside the lib folder are written in-house.

List of packages (Standard Libraries) that has been included into the project are as follow:

• notebook: 6.4.8
• keras: 2.4.3
• keras_tuner: 1.1.0
• matplotlib: 3.4.3
• numpy: 1.20.3
• pandas: 1.3.4
• scikit-learn: 0.24.2
• scipy: 1.7.3
• tensorboard: 2.6.0
• tensorflow: 2.3.0