In this project, I prepared a prototype machine learning model for Zyfra, a company that develops efficiency solutions for the heavy industry.
The goal of the model was to predict the amount of gold extracted from gold ore using the available extraction and purification data. This model helped optimize production and identify non-profitable parameters.
To complete the project:
I prepared and cleaned the necessary data.
I performed exploratory data analysis to understand the dataset, identify relevant relationships, and assess variable quality.
I developed and trained a machine learning model capable of predicting the amount of extracted gold with good accuracy.
Throughout the project, I used the documentation from pandas, matplotlib, and scikit-learn to support data preparation, visualization, and model development.
(content in spanish)