Industrial Copper Modelling Project - Regression & Classification Model

Problem Statement:

The copper industry deals with less complex data related to sales and pricing. However, this data may suffer from issues such as skewness and noisy data, which can affect the accuracy of manual predictions. Dealing with these challenges manually can be time-consuming and may not result in optimal pricing decisions. A machine learning regression model can address these issues by utilizing advanced techniques such as data normalization, feature scaling, and outlier detection, and leveraging algorithms that are robust to skewed and noisy data.

Another area where the copper industry faces challenges is in capturing the leads. A lead classification model is a system for evaluating and classifying leads based on how likely they are to become a customer . You can use the STATUS variable with WON being considered as Success and LOST being considered as Failure and remove data points other than WON, LOST STATUS values."""

Link to the Dataset : https://docs.google.com/spreadsheets/d/18eR6DBe5TMWU9FnIewaGtsepDbV4BOyr/edit?gid=462557918#gid=462557918

Download the dataset and store it in the project folder before proceeding

Setting up the conda environment

conda create -p copperenv python==3.10

Activate the conda environment

conda activate copperenv\

Install all the requirements

pip install -r requirements.txt

Regression Model & Classification Model

Training - Regression

Path : \Industrial Copper Mining\regression

python reg_training.py

Model Training would be completed and the following pickle files would be generated

pickle file path : \Industrial Copper Mining\regression\reg_pickle_files

boxcox_params.pkl, capping_bounds.pkl, label_encoders.pkl, rf_model.pkl, scaler.pkl

Training - Classification

Path : \Industrial Copper Mining\classification

python class_training.py

Model Training would be completed and the following pickle files would be generated

pickle file path : \Industrial Copper Mining\regression\class_pickle_files

boxcox_params.pkl, capping_bounds.pkl, label_encoders.pkl, rf_model.pkl, scaler.pkl

Model Testing

Run the Streamlit app, pass the required inputs and click on Predict

In order to test the Regression Model click on Regressoon

In order to test the Classification Model click on Classification

Path : \Industrial Opper Mining

streamlit run app.py