My aim here is to build a model using various approaches that will be able to predict an ore's purity and impurity using the different column values as input.
The dataset used in this project is taken from the Kaggle website.
Dataset Link:- https://www.kaggle.com/datasets/edumagalhaes/quality-prediction-in-a-mining-process
In this dataset there is a table which contains 24 columns and around 735000 rows.
Some of the columns are:% Iron Feed, % Silica Feed, Starch Flow, Ore Pulp Flow, Floatation columns and so on.
The contributor is expected to add details of the project in a table mentioning the project name, the contributor(one who uploaded), the models used and metric scores of the same. Additional fields like active issues will be appreciated.
The aim of this issue is to create a folder which contains all the classification model templates such as Logistic Regression, SVM, Random Forest Classifier etc.
Big Market sales prediction model needs to be evaluated with well defined accuracy and other metrics. The contributor is expected to write clear code with proper titles and descriptions of the task performed.
A proper readme is expected for the project. A text file containing basic information about the same is already given with the file, any contributor wishing to do the task is expected to write a proper readme with proper formatting.