https://github.com/nurulashraf/polynomial-regression-manufacturing
A Python project implementing polynomial regression to analyse and predict manufacturing-related data. Features include data preprocessing, model training, and visualisation of results. Ideal for exploring machine learning applications in manufacturing process optimisation.
https://github.com/nurulashraf/polynomial-regression-manufacturing
data-analysis data-visualization machine-learning manufacturing polynomial-regression predictive-modeling process-optimization python regression-models scikit-learn
Last synced: 3 months ago
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A Python project implementing polynomial regression to analyse and predict manufacturing-related data. Features include data preprocessing, model training, and visualisation of results. Ideal for exploring machine learning applications in manufacturing process optimisation.
- Host: GitHub
- URL: https://github.com/nurulashraf/polynomial-regression-manufacturing
- Owner: nurulashraf
- License: mit
- Created: 2025-01-04T12:24:22.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2025-01-20T00:21:58.000Z (over 1 year ago)
- Last Synced: 2025-02-28T21:07:29.998Z (over 1 year ago)
- Topics: data-analysis, data-visualization, machine-learning, manufacturing, polynomial-regression, predictive-modeling, process-optimization, python, regression-models, scikit-learn
- Language: Jupyter Notebook
- Homepage:
- Size: 723 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Polynomial Regression in Manufacturing Analysis
This project demonstrates the application of Polynomial Regression to analyze and predict manufacturing performance metrics. It provides a practical implementation of this regression technique using Python libraries, visualizations, and real-world manufacturing data.
## Features
- Polynomial regression modeling for non-linear data.
- Data preprocessing and exploratory analysis.
- Model evaluation metrics for performance comparison.
- Visualization of regression curves and predictions.
## Requirements
- Python 3.x
- Libraries: numpy, pandas, matplotlib, sklearn
## Usage
1. Clone the repository:
```
git clone
```
2. Install required dependencies:
```
pip install -r requirements.txt
```
3. Open the Jupyter Notebook:
```
jupyter notebook Polynomial_Regression_Manufacturing.ipynb
```
4. Follow the step-by-step implementation within the notebook.
## License
This project is licensed under the [MIT License](LICENSE). See the LICENSE file for details.