https://github.com/praveendecode/industrial_copper_modeling

Enhance data analysis and machine learning skills in the 'Industrial Copper Modeling' project. Tackle complex sales data challenges, employ regression models for pricing predictions, and master lead classification for targeted customer solutions
https://github.com/praveendecode/industrial_copper_modeling

classification exploratory-data-analysis googlecolaboratory hyperparameter-tuning machine-learning python regression streamlit-webapp

Last synced: 29 days ago
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Enhance data analysis and machine learning skills in the 'Industrial Copper Modeling' project. Tackle complex sales data challenges, employ regression models for pricing predictions, and master lead classification for targeted customer solutions

• Host: GitHub
• URL: https://github.com/praveendecode/industrial_copper_modeling
• Owner: praveendecode
• Created: 2023-09-13T05:06:18.000Z (over 1 year ago)
• Default Branch: main
• Last Pushed: 2023-12-21T12:12:01.000Z (over 1 year ago)
• Last Synced: 2025-02-09T13:35:11.653Z (3 months ago)
• Topics: classification, exploratory-data-analysis, googlecolaboratory, hyperparameter-tuning, machine-learning, python, regression, streamlit-webapp
• Language: Jupyter Notebook
• Homepage:
• Size: 12.4 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# Industrial Copper Modeling

   ![image](https://github.com/praveendecode/Industrial_Copper_Modeling/assets/95226524/b0cbc601-570f-4d6b-9346-a8abdbb615fb)

# Overview

  Enhance data analysis and machine learning skills in the 'Industrial Copper Modeling' project. Tackle complex sales data challenges, employ regression models for pricing predictions, and master lead classification for targeted customer solutions

# Features

 ## 1. Data Preprocessing:

- Gain a deep understanding of dataset variables and types.

- Handle missing data with appropriate strategies.

- Prepare categorical features through encoding and data type conversion.

- Address skewness and ensure data balance.

- Identify and manage outliers.

- Resolve date discrepancies for data integrity.

 ## 2. Exploratory Data Analysis (EDA) and Feature Engineering:

- Visualize and correct skewness.

- Identify and rectify outliers.

- Feature improvement and creation for more effective modeling.

 ## 3. Classification:

- Success and Failure Classification: Focusing on 'Won' and 'Lost' status.

- Algorithm Assessment: Evaluating algorithms for classification.

- Algorithm Selection: Choosing the Random Forest Classifier.

- Hyperparameter Tuning: Fine-tuning with GridSearchCV and cross-validation.

- Model Accuracy and Metrics: Assessing performance and metrics.

- Model Persistence: Saving the model for future use.

 ## 4. Regression:

- Algorithm Assessment: Identifying algorithms for regression.

- Algorithm Selection: Opting for the Random Forest Regressor.

- Hyperparameter Tuning: Fine-tuning with GridSearchCV and cross-validation.

- Model Accuracy and Metrics: Evaluating regression model performance.

- Model Persistence: Saving the regression model for future applications.

# Getting Started

 ## 1. Clone the repository:

          https://github.com/praveendecode/Industrial_Copper_Modeling

##  2. Install required packages:

          pip install -r requirements.txt

          

##  3. Run the Streamlit app:

          streamlit run app.py

    

##  4. Access the app in your browser:

          http://localhost:8501

# Skills Covered

   - Python

   - Numpy

   - Pandas

   - Scikit-Learn

   - Matplotlib

   - Seaborn

   - Pickle

   - Streamlit

   - Docker

# Results

- Classification: Achieved 98.999% accuracy with ExtraTrees Forest Classifier.

- Regression: Achieved 98.3% accuracy with ExtraTrees Forest Regressor.

### This project not only demonstrates data analysis and machine learning skills but also showcases practical applications for solving complex challenges in the manufacturing industry.