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---
title: Iris Flower Classifier
tags:
- iris
- classification
- decision-tree
- code
- machine-learning
- scikit-learn
license: mit
---
# Iris Flower Classifier
## Visual Reference
## Model Overview
The Iris Flower Classifier is a machine learning model that predicts the species of an iris flower based on its sepal and petal dimensions. The model is built using a Decision Tree Classifier trained on the well-known Iris dataset.
## Model Details
- **Model Type**: Decision Tree Classifier
- **Input Features**:
- Sepal Length (cm)
- Sepal Width (cm)
- Petal Length (cm)
- Petal Width (cm)
- **Output**: Species of the iris flower (Setosa, Versicolor, Virginica)
## Training Data
- **Dataset**: The model was trained on the Iris dataset, which contains 150 samples of iris flowers, each with four features and a corresponding species label.
- **Source**: [UCI Machine Learning Repository](https://archive.ics.uci.edu/ml/datasets/iris)
## Intended Use
This model is intended for educational purposes and can be used to:
- Predict the species of an iris flower based on its measurements.
- Serve as an example of using a Decision Tree Classifier in Python.
## Limitations
- The model may not perform well on unseen data that differs significantly from the training data.
- It is specifically designed for classifying iris flowers and may not generalize to other types of flowers or datasets.
## How to Use
You can use this model through a Gradio interface. Simply enter the measurements of the iris flower, and the model will predict the species.
## How to open this model
- By using this command
- !git clone https://huggingface.co/shahad23/IrisFlowerModel
- then copy the content of IrisModel.py then run it.
### Example
To predict the species, input the following:
- Sepal Length: 5.1
- Sepal Width: 3.5
- Petal Length: 1.4
- Petal Width: 0.2
## License
This model is licensed under the MIT License. You can use it freely, but attribution is appreciated.
## Acknowledgments
Thanks to the contributors of the Iris dataset and the developers of the scikit-learn library for making this project possible. |