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