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Passenger ship powering prediction web application with Scikit-learn, Jinja, boostrap and FastAPI

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jtkhair/Certifai_MLFullstack_Capstone

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Passenger ship powering prediction web application with Scikit-learn, Jinja, boostrap, FastAPI and Docker container

Introduction

This web application is developed based on the joint work between the Marine Technology Centre , Universiti Teknologi Malaysia (UTM) and the Hiekata Laboratory, University of Tokyo (UTokyo) under the UTM research project "Predictive Model for Ship Design and Configuration Using Machine Learning : Model Development and Validation". The details of the ship powering prediction modelling is described in the research paper presented in the International Conference on Design and Concurrent Engineering 2021 & Manufacturing System Conference 2021

Adi Maimun, Hiekata Kazuo, Jauhari Khairuddin, Siow Chee Loon and Arifah Ali, : "Estimation of Ship Powering in Preliminary Ship Design Using Graph Theory and Machine Learning Method". In the International Conference on Design and Concurrent Engineering 2021 & Manufacturing System Conference 2021, Sep 2021, Japan

Getting started

  1. Clone the repo by running this command in the terminal:
git clone https://github.com/jtkhair/Certifai_MLFullstack_Capstone
  1. Make sure to run the command git pull (if you already cloned this repo). In the cloned repo directory, run this command in the terminal:
git pull https://github.com/jtkhair/Certifai_MLFullstack_Capstone
  1. Build docker image by running below commands in the terminal (make sure docker is running):
docker build -t aishipwebapp:1.0 .
  1. Run the docker container by running below command in the terminal:
docker run -d --name aishipwebapp -p 80:80 aishipwebapp:1.0
  1. Go to the link http://127.0.0.1:80 to use the web app

  2. Input dataset *.csv file and click submit to perform the prediction. Note that the *.csv file must follow the set format

Input data format, range and description

Parameter LWL B T L/B B/T Disp CB Vs Fn P
Range 80 - 240 15 - 32 3 - 8 3.5 - 9.0 3.0 - 5.5 2500 - 32000 0.5 - 0.7 14.5 - 30.5 0.20 - 0.40 3000 - 70000

Acronym

  • Waterline Length in m, LWL
  • Breadth in m, B
  • Draught in m, T
  • Length-to-Breadth ratio, L/B
  • Breadth-to-Draught ratio, B/T
  • Displacement in t, Disp
  • Block Coefficient, CB
  • Service Speed in kn, Vs
  • Froude Number, Fn
  • Brake KiloWatt Power in kW, P

Acknowledgement

This work is funded by the Universiti Teknologi Malaysia (UTM) under the Research University Grant (RUG). Project number: Q.J130000.3851.19J88 and titled: “Predictive Model for Ship Design and Configuration Using Machine Learning - Model Development and Validation”

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Passenger ship powering prediction web application with Scikit-learn, Jinja, boostrap and FastAPI

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