/Oil-and-gas-pipeline-leakage

Primary LanguageJupyter Notebook

Final Project:

Oil and Gas.ipynb

  • This code is for my project which achieved 97% using SVM model. This word had been published in Computational Journal.
  • For more details and an explanation of the work find the paper at the link: https://www.mdpi.com/2079-3197/10/8/138

Oil-and-gas-pipeline-leakage

  • Randonmly generated data without corrosion defect.
  • Used data.csv for Polynomial and linear regression - polynomial regression.ipynb
  • Randomly generated fake_data2.csv in new.py without "CR-corrosion defect" feature.
  • Predicted "CR-corrosion defect" feature for fake_data2.csv using the .ipnyb above.
  • Final dataset is called generated_data.csv
  • Created a python script for the model - digitalModel.py
  • Created a physical model data with 50 data points- stream.csv
  • Created an endpoint for the model - app.py
  • Created an endpoint streaming to the digital model to -app2.py (http://127.0.0.1:5001/)

To get a feel and for the values to output in mm:

  • Run app.py on terminal
  • Run app2.py on terminal

To get a feel and for the values to output in NACE classification:

  • Run app_1.py on terminal
  • Run app_2.py on terminal