📊 Visualizing the Iris Dataset

📌 Overview

This project provides a comprehensive visualization of the famous Iris dataset using various Python libraries such as Matplotlib, Seaborn, and Plotly. The visualizations help in understanding the distribution and relationships among different features of the dataset.

🏗️ Features

• 📈 Histograms for feature distributions
• 📊 KDE Plots for density estimation
• 🟢 Scatter Plots for sepal and petal comparisons
• 📦 Box Plots to observe variations across species
• 🥧 Pie Chart to show species distribution
• 🔵 Bubble Chart for categorical representation

🛠️ Installation & Usage

🔹 Prerequisites

Ensure you have Python installed along with the required libraries:

pip install pandas numpy matplotlib seaborn plotly scikit-learn

🔹 Running the Script

Clone the repository and navigate to the project directory:

git clone https://github.com/1Ayanabil1/iris-visualization.git
cd iris-visualization

Run the visualization script:

python visualization.py

📂 Dataset

The dataset used is the Iris dataset, available as Iris.csv. It consists of 150 samples with the following attributes:

• SepalLengthCm
• SepalWidthCm
• PetalLengthCm
• PetalWidthCm
• Species

📷 Sample Visualizations

Here are some of the generated visualizations:

• Histograms: 📊 Feature distributions

  

• Scatter Plots: 🔍 Relationship between dimensions

  

• Box Plots: 📦 Comparative analysis across species

  

🤝 Contributing

Contributions are welcome! Feel free to fork the repository and submit a pull request.

📜 License

This project is licensed under the MIT License.

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📧 For any inquiries, reach out via [email protected]. Happy coding! 🚀