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A machine learning model based on gradient boosting decision tree for predicting heavy metal adsorption in soil.

Home Page: https://www.chemai.aropha.com/adsorption/heavy-metal-in-soil/about.html

License: MIT License

Python 81.52% Jupyter Notebook 18.48%
adsorption gradient-boosting-decision-trees heavy-metal machine-learning python3 soil

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predicting-heavy-metal-adsorption-in-soil's Issues

Outliers in dataset

There are outliers in the /data/adsorption_data_for_each_metal.xlsx.
For example, the maximum value of the pH range is 20, which appears to be included in the Pb data.
In addition to the above, the maximum value of the clay content range exceeds 100% (340%).

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