Iris Flower Classification

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
  • Built With
• Getting Started
  • Prerequisites
  • Installation
• Usage
• 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.

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.