Industrial-Copper-Modelling

Copper Industry Sales and Leads Prediction Project

Overview:

The Copper Industry Sales and Leads Prediction Project addresses challenges in managing sales and pricing data characterized by skewness and noise. Manual predictions within the industry are often time-consuming and may lack accuracy. To overcome these challenges, this project focuses on the development of machine learning models.

Tools Used:

• Python: Facilitates versatile programming capabilities.
• Pandas and NumPy: These libraries will be used for data manipulation and preprocessing.
• Scikit-Learn: A powerful machine learning library that includes tools for regression and classification models.
• Streamlit: A user-friendly library for creating web applications with minimal code, perfect for building an interactive interface for our models.

Steps in the Solution:

1. Exploring Data:

• Identify and address skewness and outliers in the dataset.

2. Data Preprocessing:

• Transform data into a suitable format.
• Clean and preprocess data, handling missing values.

3. Regression Model:

• Utilize machine learning regression to predict the continuous variable 'Selling_Price.'
• Apply advanced techniques like data normalization and feature scaling.

4. Classification Model:

• Develop a classification model to predict lead statuses (WON or LOST).

5. Streamlit Web App:

• Create an interactive Streamlit web page.
• Input column values to get predicted 'Selling_Price' or lead status (WON/LOST).

Conclusion:

This project addresses critical challenges in the copper industry by employing machine learning techniques for sales and lead prediction. The developed models enhance decision-making efficiency, providing a robust solution for accurate 'Selling_Price' predictions and lead classification.