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Machine Learning Guide for Oil and Gas Using Python
book

Machine Learning Guide for Oil and Gas Using Python

by Hoss Belyadi, Alireza Haghighat
April 2021
Intermediate to advanced
476 pages
12h 36m
English
Gulf Professional Publishing
Content preview from Machine Learning Guide for Oil and Gas Using Python

Overview

In this chapter, the following supervised machine learning (ML) algorithms will be discussed:
  • (i) Multilinear regression
  • (ii) Logistic regression
  • (iii) K-nearest neighbor (KNN)
  • (iv) Support vector machine (SVM)
  • (v) Decision tree
  • (vi) Random forest
  • (vii) Extra trees
  • (viii) Gradient boosting
  • (ix) Extreme gradient boosting
  • (x) Adaptive gradient boosting
After discussing the concept for each algorithm and providing the mathematical background, scikit-learn library will be used to demonstrate the implementation of each algorithm with multiple, practical oil and gas problems such as production optimization, human resource employee sustainability prediction, sonic log (shear wave and compression wave travel times) prediction, economic (net present value) ...
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Publisher Resources

ISBN: 9780128219300