https://github.com/nurulashraf/svm-iris-flower-classification
SVM classifier for Iris flower dataset. Trains, evaluates, and saves a model to classify Setosa, Versicolor, and Virginica species based on petal and sepal measurements. Built with Python, scikit-learn, and Jupyter Notebook.
https://github.com/nurulashraf/svm-iris-flower-classification
classification data-science iris-dataset machine-learning model-training supervised-learning svm
Last synced: about 1 year ago
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SVM classifier for Iris flower dataset. Trains, evaluates, and saves a model to classify Setosa, Versicolor, and Virginica species based on petal and sepal measurements. Built with Python, scikit-learn, and Jupyter Notebook.
- Host: GitHub
- URL: https://github.com/nurulashraf/svm-iris-flower-classification
- Owner: nurulashraf
- License: mit
- Created: 2025-04-29T06:56:04.000Z (about 1 year ago)
- Default Branch: main
- Last Pushed: 2025-04-29T07:08:10.000Z (about 1 year ago)
- Last Synced: 2025-04-29T07:45:17.545Z (about 1 year ago)
- Topics: classification, data-science, iris-dataset, machine-learning, model-training, supervised-learning, svm
- Language: Jupyter Notebook
- Homepage:
- Size: 0 Bytes
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# SVM Iris Flower Classification
This project uses a Support Vector Machine (SVM) model to classify Iris flowers into three species, *Setosa*, *Versicolor*, and *Virginica*, based on features such as sepal and petal length and width. The dataset is the classic Iris dataset from scikit-learn.
---
## Project Structure
- **`notebooks/`**: Jupyter notebooks for data analysis, feature engineering, and model building.
- **`README.md`**: Project overview and usage instructions.
---
## Features
- Loads and explores the Iris dataset
- Preprocesses data and splits into train/test sets
- Trains an SVM classifier using scikit-learn
- Evaluates model performance (accuracy, confusion matrix)
- Saves the trained model using pickle
- Easy to modify for experimenting with other models
---
## Tools & Libraries Used
- Python 3.x
- Jupyter Notebook
- scikit-learn
- pandas
- numpy
- matplotlib
- seaborn
- pickle
---
## How to Use
### 1. Clone the repository
```bash
git clone https://github.com/nurulashraf/svm-iris-flower-classification.git
cd svm-iris-flower-classification
```
### 2. Create a virtual environment (optional but recommended)
```bash
python -m venv venv
source venv/bin/activate # On Windows: venv\Scripts\activate
```
### 3. Install dependencies
```bash
pip install -r requirements.txt
```
### 4. Run the notebook
Open `svm_iris_flower_classification.ipynb` in Jupyter Notebook or JupyterLab and run the cells.
---
## License
This project is licensed under the [MIT License](LICENSE).