GitHub - timmehlui/Machine-Learning-Geological-Mapping-Soil-Geochemistry: Machine learning project that predicts underlying geology using soil sample geochemistry. Tests benefits of topographic data, sampling methods, machine learning algorithms, and multiple classifier systems.

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Machine learning project that predicts underlying geology using soil sample geochemistry. Tests benefits of topographic data, sampling methods, machine learning algorithms, and multiple classifier systems.

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Name		Name	Last commit message	Last commit date
Latest commit History 26 Commits
TestData.csv		TestData.csv
alrCorrelationAnalysis.py		alrCorrelationAnalysis.py
instructionGuide.txt		instructionGuide.txt
mcsprob.py		mcsprob.py
pipelineComplexMCS.py		pipelineComplexMCS.py
pipelineSamplingMethod.py		pipelineSamplingMethod.py
readme.txt		readme.txt
sameClassOrderNine.py		sameClassOrderNine.py

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Details
Code related to manuscript titled "Applying machine learning methods to predict geology using soil sample geochemistry", authored by Timothy C.C. Lui, Daniel D. Gregory, Marek Anderson, Well-Shen Lee, and Sharon A. Cowling.

Requirements
Python 3.7.3
scikit-learn 0.23
imblearn 0.5
numpy 1.19.1
pandas 1.1.3
matplotlib 3.3.1

Files
alrCorrelationAnalysis.py is used for data cleaning of correlated features.
pipelineSamplingMethod.py compares the various sampling methods used.
pipelineComplexMCS.py compares the machine learning algorithms and the multiple classifier systems.
sameClassOrderNine.py is a function used in pipelineComplexMCS.py.
TestData.csv is randomized data with same format of real data to test the functionality of the code.