# IRIS-Flower-Classification

**Repository Path**: markets2022/IRIS-Flower-Classification

## Basic Information

- **Project Name**: IRIS-Flower-Classification
- **Description**: 鸢尾花
- **Primary Language**: Unknown
- **License**: MIT
- **Default Branch**: main
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2025-09-04
- **Last Updated**: 2025-09-04

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# Iris Flower Classification

[![Iris Flowers](https://repository-images.githubusercontent.com/158275423/9a32d741-51c7-4573-9799-8d933ee642c6)](https://usman3454.pythonanywhere.com/)


This project classifies Iris flowers into different species based on their sepal and petal measurements. It uses a machine learning model to make predictions.

## Table of Contents

- [About the Project](#about-the-project)
  - [Built With](#built-with)
- [Getting Started](#getting-started)
  - [Prerequisites](#prerequisites)
  - [Installation](#installation)
- [Usage](#usage)
- [License](#license)

## About the Project

The Iris Flower Classification project is a classic example of a machine learning classification task. It involves predicting the species of Iris flowers based on their sepal length, sepal width, petal length, and petal width.

### Built With

- Python
- Scikit-Learn
- Flask (for web application)

## Getting Started

These instructions will help you set up and run the project on your local machine.

### Prerequisites

You need to have Python installed on your system. If you haven't already, you can download it from [python.org](https://www.python.org/downloads/).

### Installation

1. Clone the repository:

   ```
   git clone https://github.com/usmanbvp/iris-flower-classification.git``````
2. Change directory:

  ```cd iris-flower-classification```
3. Install the required packages:
  ```pip install -r requirments.txt```
   The `requirements.txt` file contains a list of necessary packages and their versions.
  
### Usage
1. Run the web appilcation:
    ```python app.py```
2. Open your web browser and got to http://localhost:5000.

3. Use the web interface to input sepal and petal measurements to classify iris flowers.

### Deployment
When you are finished running it on your local machine, you should host your project online. We highly recommend deploying on PythonAnywhere for practice because it's free to use.

### License
This project is licensed under the MIT License - see the `LICENSE` file for details.