Published June 28, 2024
| Version v1
Software
Open
Machine learning models for predicting Estonian phosphorite dissolution in hydrochloric acid
Creators
- 1. Department of Chemistry and Biotechnology
- 2. Department of Materials and Environmental Technology
Description
This collection contains ML model trained to predict phosphorite concentration in hydrochloric acid and a Python notebook modelling.ipynb that creates models and gives an example of how to upload and use them.
Abbreviation:
Linear regression - lr
Linear polynomial regression of second order - lpr2
Support vector regression - svr
Support vector regression with polynomial features - svr_pl2
Random forest regression - rf
Random forest regression with polynomial features - rf_pl2
CatBoost regression - catr
CatBoost regression with polynomial features - catr_pl2
Neural net - NN
Files
modeling.ipynb
Files
(1.3 MB)
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