https://github.com/1ayanabil1/iris-visualization
This repository focuses on visualizing the Iris dataset using various data visualization techniques. It includes histograms, scatter plots, box plots, pie charts, bubble charts, and KDE plots to provide insights into the dataset’s structure. The project utilizes Matplotlib, Seaborn, Plotly, and Scikit-learn to generate insightful visualizations.
https://github.com/1ayanabil1/iris-visualization
analytics clustering data-analysis data-science data-visualization datavisualization-project datavisualizations eda exploratory-data-analysis machine-learning machinelearning-python python
Last synced: 4 months ago
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This repository focuses on visualizing the Iris dataset using various data visualization techniques. It includes histograms, scatter plots, box plots, pie charts, bubble charts, and KDE plots to provide insights into the dataset’s structure. The project utilizes Matplotlib, Seaborn, Plotly, and Scikit-learn to generate insightful visualizations.
- Host: GitHub
- URL: https://github.com/1ayanabil1/iris-visualization
- Owner: 1AyaNabil1
- License: mit
- Created: 2022-12-02T13:15:23.000Z (over 2 years ago)
- Default Branch: main
- Last Pushed: 2025-02-22T23:20:14.000Z (4 months ago)
- Last Synced: 2025-02-23T00:25:26.192Z (4 months ago)
- Topics: analytics, clustering, data-analysis, data-science, data-visualization, datavisualization-project, datavisualizations, eda, exploratory-data-analysis, machine-learning, machinelearning-python, python
- Language: Jupyter Notebook
- Homepage:
- Size: 1.74 MB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- Contributing: CONTRIBUTING.md
- License: LICENSE
- Code of conduct: CODE_OF_CONDUCT.md
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README
# 📊 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:
```bash
pip install pandas numpy matplotlib seaborn plotly scikit-learn
```
### 🔹 Running the Script
Clone the repository and navigate to the project directory:
```bash
git clone https://github.com/1Ayanabil1/iris-visualization.git
cd iris-visualization
```
Run the visualization script:
```bash
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]](mailto:[email protected]). Happy coding! 🚀