Iris-Classification 🌸

This project is a machine learning-based classifier built to predict the species of an Iris flower based on its physical characteristics. The classifier leverages the classic Iris Dataset and is implemented using Jupyter Notebook.

Table of Contents

• Overview
• Dataset
• Project Structure
• Requirements
• Installation
• Usage
• Model Performance
• Results
• Future Improvements
• References

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Overview

This project demonstrates a supervised learning approach to classify the species of an Iris flower based on four key features:

1. Sepal Length
2. Sepal Width
3. Petal Length
4. Petal Width

Using these features, the classifier can predict the species among Setosa, Versicolour, and Virginica.

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Dataset

The Iris Dataset is widely used in data science and machine learning for classification tasks. It consists of 150 samples with the following columns:

• Features: Sepal Length, Sepal Width, Petal Length, Petal Width
• Target: Species (Setosa, Versicolour, Virginica)

The dataset is available in the UCI Machine Learning Repository.

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Project Structure

Iris-Classification/
├── notebooks/
│   └── iris_classification.ipynb   # Jupyter Notebook with data exploration, model training, and evaluation
├── README.md                       # Project documentation
└── requirements.txt                # Python dependencies

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Requirements

To run this notebook, you’ll need Python 3.8+ and the following libraries:

• Jupyter Notebook
• NumPy
• Pandas
• Scikit-learn
• Matplotlib (for visualizations)

Install all dependencies using:

pip install -r requirements.txt

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Installation

Clone the repository and navigate into the project directory:

git clone https://github.com/username/Iris-Classification.git
cd Iris-Classification

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Usage

1. Launch Jupyter Notebook
   Open the Jupyter Notebook environment:

   jupyter notebook

2. Open the Notebook
   In the Jupyter interface, navigate to the notebooks/iris_classification.ipynb file and open it.

3. Run Cells Sequentially
   Execute each cell to load the dataset, explore data, train the model, and evaluate its performance.

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Model Performance

The classifier was evaluated primarily on accuracy. Below are some example results based on common classifiers:

Model	Accuracy
Logistic Regression	95%
Support Vector Machine (SVM)	96%
Decision Tree	94%

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Results

The models performed well on the dataset, achieving high accuracy for classifying the three Iris species. The SVM model performed the best in this setup.

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Future Improvements

Consider the following potential improvements:

• Hyperparameter Optimization: Tune the model for better performance.
• Model Comparison: Experiment with ensemble methods like Random Forest and Gradient Boosting.
• Visualizations: Add more visualizations for feature importance and decision boundaries.

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References

• Iris Dataset - UCI Machine Learning Repository
• Scikit-learn Documentation

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