The purpose of this project is to prepare a prototype of a machine learning model for Zyfra, a company that develops efficiency solutions for the heavy industry. The model will aim to predict the amount of gold recovered from gold ore. The features we will use will be data on gold extraction and purification. The goal is to have the model optimize the production and eliminate unprofitable parameters.
Overall, we were able to work with the data we received to complete the project. We ensured the recovery column was calculated correctly, by comparing it with our calculated values. We looked at the distribution of concentrations for the various metals, and saw anomalies, which where removed. The data illustrated the increase in gold concentrations in the final product, and a small amount of gold in the tails. We also looked at the recovery of gold, and compared it with the other metals. Finally, we successfully trained a model that could predict the gold recovery, and we found the decision tree to be the best model to use. Therefore, Zyfra can use this model to optimize their gold ore refining process.