https://github.com/nurulashraf/comparative-regression-fish-dataset
This analysis compares multiple linear regression and polynomial regression models using fish measurement data. It evaluates prediction accuracy through MSE, RMSE, R², and Adjusted R², and tests model performance on newly generated dummy data.
https://github.com/nurulashraf/comparative-regression-fish-dataset
data-science linear-regression machine-learning polynomial-regression
Last synced: about 2 months ago
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This analysis compares multiple linear regression and polynomial regression models using fish measurement data. It evaluates prediction accuracy through MSE, RMSE, R², and Adjusted R², and tests model performance on newly generated dummy data.
- Host: GitHub
- URL: https://github.com/nurulashraf/comparative-regression-fish-dataset
- Owner: nurulashraf
- License: mit
- Created: 2025-04-19T15:05:38.000Z (2 months ago)
- Default Branch: main
- Last Pushed: 2025-04-21T14:38:03.000Z (2 months ago)
- Last Synced: 2025-04-21T15:37:44.627Z (2 months ago)
- Topics: data-science, linear-regression, machine-learning, polynomial-regression
- Language: Jupyter Notebook
- Homepage:
- Size: 13.7 MB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Fish Dataset — Multiple Linear & Polynomial Regression Comparison
This project explores and compares the performance of **Multiple Linear Regression** and **Polynomial Regression** models using a fish measurement dataset. The goal is to predict outcomes and evaluate model accuracy using metrics like **Mean Squared Error (MSE)**, **Root Mean Squared Error (RMSE)**, **R² Score**, and **Adjusted R²**. Dummy data is also generated to test model predictions beyond the original dataset.
---
## Project Structure
- **`data/`**: Contains the dataset used for analysis and prediction.
- **`notebooks/`**: Jupyter notebooks for data analysis, feature engineering, and model building.
- **`README.md`**: Project overview and usage instructions.
---
## Features
- Compares **Multiple Linear Regression** and **Polynomial Regression** on the same dataset.
- Uses real-world fish measurement data for training and testing.
- Generates dummy data to test model predictions.
- Outputs key evaluation metrics: MSE, RMSE, R², and Adjusted R².
- Saves trained models for reuse.
---
## Tools & Libraries
- Python
- Pandas
- NumPy
- Scikit-learn
- Matplotlib
- Joblib
---
## How to Use
1. Clone this repository:
```bash
git clone https://github.com/nurulashraf/comparative-regression-fish-dataset.git
cd comparative-regression-fish-dataset
```
2. Install the required Python packages:
```bash
pip install pandas numpy scikit-learn matplotlib joblib
```
3. Open the Jupyter notebook:
```bash
jupyter notebook comparative_regression_fish_dataset.ipynb
```
4. Run through the notebook to:
- Train both models.
- Evaluate performance.
- Generate predictions for dummy data.
- Save or load pre-trained models using `joblib`.
---
## License
This project is licensed under the [MIT License](LICENSE).