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https://github.com/mohini1403/iris-classifier
Iris Classifier Web App
https://github.com/mohini1403/iris-classifier
flask html5 ml python3 sckiit-learn
Last synced: 7 days ago
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Iris Classifier Web App
- Host: GitHub
- URL: https://github.com/mohini1403/iris-classifier
- Owner: MOHINI1403
- Created: 2024-03-28T03:44:14.000Z (8 months ago)
- Default Branch: main
- Last Pushed: 2024-03-28T04:10:04.000Z (8 months ago)
- Last Synced: 2024-03-29T04:30:02.086Z (8 months ago)
- Topics: flask, html5, ml, python3, sckiit-learn
- Language: Jupyter Notebook
- Homepage: https://iris-classifier-iy3n.onrender.com
- Size: 524 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
Certainly! Below is a sample README file for your Iris classification ML project:
---
# Iris Classification ML Project
This project implements a simple Flask web application for classifying Iris flowers based on their sepal and petal dimensions. The application uses a Random Forest classifier trained on the famous Iris dataset.
## Features
- Allows users to input sepal length, sepal width, petal length, and petal width via a web form.
- Provides real-time classification of Iris species based on user input.
- Displays the predicted species along with an image of the corresponding Iris flower.
## Prerequisites
- Python 3.x
- Flask
- scikit-learn
- NumPy
## Installation
1. Clone this repository to your local machine:
```bash
git clone https://github.com/MOHINI1403/iris-classifier.git
```
2. Navigate to the project directory:
```bash
cd iris-classifier
```
3. Install the required dependencies:
```bash
pip install -r requirements.txt
```
## Usage
1. Start the Flask server by running:
```bash
python deploy.py
```
2. Open a web browser and go to `http://localhost:5000` to access the application.
3. Enter the sepal and petal dimensions in the provided form fields and click "Classify".
4. View the predicted species along with an image of the corresponding Iris flower.
## Project Structure
- `deploy.py`: Main Flask application file containing the routes and logic for prediction.
- `templates/`: Directory containing HTML templates for rendering the web pages.
- `index.html`: HTML template for the input form page.
- `result.html`: HTML template for displaying the classification result.
- `static/`: Directory containing static files such as CSS stylesheets and images.
- `images/`: Directory containing images of Iris flowers for visualization.
- `requirements.txt`: File containing the Python dependencies required for the project.
## Contributing
Contributions are welcome! Feel free to open issues or pull requests for any improvements or bug fixes.
## License
This project is licensed under the [MIT License](LICENSE).
---
Feel free to customize this README file according to your specific project details and preferences.