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

Clone this repository to your local machine:

git clone https://github.com/MOHINI1403/iris-classifier.git

Navigate to the project directory:

cd iris-classifier

Install the required dependencies:

pip install -r requirements.txt

Usage

Start the Flask server by running:

python deploy.py

Open a web browser and go to http://localhost:5000 to access the application.
Enter the sepal and petal dimensions in the provided form fields and click "Classify".
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.

Feel free to customize this README file according to your specific project details and preferences.