https://github.com/zuruoke/quality_prediction_in-a_mining_process
Quality Prediction in a Mining Process Using Regressor Model
https://github.com/zuruoke/quality_prediction_in-a_mining_process
Last synced: 8 months ago
JSON representation
Quality Prediction in a Mining Process Using Regressor Model
- Host: GitHub
- URL: https://github.com/zuruoke/quality_prediction_in-a_mining_process
- Owner: zuruoke
- Created: 2020-02-15T13:21:46.000Z (over 6 years ago)
- Default Branch: master
- Last Pushed: 2020-02-15T13:28:35.000Z (over 6 years ago)
- Last Synced: 2024-12-27T11:32:21.364Z (over 1 year ago)
- Language: Jupyter Notebook
- Size: 10.7 KB
- Stars: 1
- Watchers: 2
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
Quality Prediction in a Mining Process Using Regressor Model
Froth flotation is a process for selectively separating hydrophobic materials from hydrophilic.
Historically this was first used in the mining industry and is very common in a mining plant, where it was one of the great enabling technologies of the 20th century.
The development of froth flotation has improved the recovery of valuable minerals, such as copper- and lead-bearing minerals.
Along with mechanized mining, it has allowed the economic recovery of valuable metals from much lower grade ore than previously.
This dataset I'll be using is about a flotation plant which is a process used to concentrate the iron ore.
The main goal is to use this data to predict how much impurity is in the ore concentrate.
As this impurity is measured every hour, if I can predict how much silica (impurity) is in the ore concentrate, this can help the engineers, giving them early information to take actions (empowering!).
Hence, they will be able to take corrective actions in advance (reduce impurity, if it is the case) and also help the environment (reducing the amount of ore that goes to tailings as you reduce silica in the ore concentrate).
In this kernel, I'll:
1. Prepare the data for machine learning
2. Train a model using a Regressor Model
3. Measure & optimize the accuracy of your model