Summary

This project involves a data analysis to evaluate and improve the accuracy of gold recovery calculations in a processing plant, as well as the development of machine learning models to predict recovery in future operations.

The analysis concludes that the gold recovery calculations in the original data are accurate and reliable. Additionally, the Random Forest model stands out as the best option for predicting gold recovery due to its high accuracy and generalization capability.

Github repository