https://github.com/kozuedoingregression/iris-species-classification
Machine Learning Model for Iris Species Classification based on Measurements
https://github.com/kozuedoingregression/iris-species-classification
flask iris-classification iris-dataset machine-learning python3
Last synced: 5 months ago
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Machine Learning Model for Iris Species Classification based on Measurements
- Host: GitHub
- URL: https://github.com/kozuedoingregression/iris-species-classification
- Owner: kozuedoingregression
- Created: 2023-11-06T19:33:25.000Z (over 2 years ago)
- Default Branch: main
- Last Pushed: 2024-11-18T13:36:36.000Z (over 1 year ago)
- Last Synced: 2025-05-17T03:15:38.267Z (10 months ago)
- Topics: flask, iris-classification, iris-dataset, machine-learning, python3
- Language: Python
- Homepage: https://iris-species-classification-4sfc.onrender.com/
- Size: 192 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Iris Species Classification Web App 🌸
A Flask-based web application that predicts Iris flower species using machine learning. The application uses a trained model to classify Iris flowers into three species: Setosa, Versicolor, and Virginica, based on their sepal and petal measurements.
## Features ✨
- Interactive web interface for input measurements
- Real-time predictions using machine learning
- Simple and intuitive design
## Demo 🚀
## Installation 🛠️
1. Clone the repository:
```bash
git clone https://github.com/kozuedoingregression/Iris-Species-Classification.git
cd Iris-Species-Classification
```
2. Create and activate a virtual environment:
```bash
python -m venv venv
source venv/bin/activate # On Windows: venv\Scripts\activate
```
3. Install required packages:
```bash
pip install -r requirements.txt
```
## Usage 💻
1. Start the Flask application:
```bash
python app.py
```
2. Enter the following measurements in centimeters:
- Sepal Length
- Sepal Width
- Petal Length
- Petal Width
3. Click "Predict" to see the classification result
4. Run tests
```bash
python -m unittest testing.py
```
## Project Structure 📁
```
iris-classification/
├── database/
│ ├── Iris.xls
├── model/
│ ├──IrisFlowerClaffification.pkl
├── templates/
│ ├── index.html
├── app.py # Flask application
├── requirements.txt
├── testing.py
```
## Model Performance 📊
- The model was trained using scikit-learn's Logistic Regression on the [Iris Dataset](https://www.kaggle.com/datasets/saurabh00007/iriscsv).
- Accuracy: 97%
- [NoteBook](https://www.kaggle.com/code/shashanknecrothapa/iris-flower-classification)
## Technical Details 🔧
- **Framework**: Flask
- **Machine Learning**: scikit-learn
- **Model**: Logistic Regression
- **Dataset**: [Iris Dataset](https://www.kaggle.com/datasets/saurabh00007/iriscsv)
- **Frontend**: HTML, CSS, JavaScript
## Requirements 📋
- Python 3.8+
- Flask
- scikit-learn
- pandas
- numpy
## Development 👨💻
To contribute to this project:
1. Fork the repository
2. Create a new branch
3. Make your changes
4. Submit a pull request
## Contact 📧
For questions or feedback, please contact:
- X: [kozue](https://x.com/0xaa248)
- GitHub: [kozuedoingregression](https://github.com/kozuedoingregression)