https://github.com/onlinebunker/iris-flower
Exploratory Data Analysis of Iris Flower Classification Data
https://github.com/onlinebunker/iris-flower
data-visualization eda pandas
Last synced: about 2 months ago
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Exploratory Data Analysis of Iris Flower Classification Data
- Host: GitHub
- URL: https://github.com/onlinebunker/iris-flower
- Owner: OnlineBunker
- Created: 2025-06-15T14:32:00.000Z (about 1 year ago)
- Default Branch: main
- Last Pushed: 2025-06-15T15:36:51.000Z (about 1 year ago)
- Last Synced: 2025-06-15T15:45:39.719Z (about 1 year ago)
- Topics: data-visualization, eda, pandas
- Language: Jupyter Notebook
- Homepage:
- Size: 700 KB
- Stars: 0
- Watchers: 0
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# 🌸 Iris Flower EDA
An exploratory data analysis (EDA) project on the famous Iris flower dataset. This notebook dives into the patterns, relationships, and statistics of the dataset's features to understand the characteristics of different Iris flower species.
---
## 📂 Project Structure
```
├── IRIS_flower_classification.ipynb # Jupyter notebook with full EDA
├── README.md # Project description
```
## 📌 Objective
To explore the Iris dataset using Python libraries and extract meaningful insights by analyzing and visualizing feature distributions and relationships.
---
## 🔍 What’s Inside
* Dataset overview and feature descriptions
* Group-wise statistics
* Data visualizations:
* Scatter plots
* Line plots
* Histograms
* KDE plots
* Box plots
* Heatmaps
---
## Images
* ScatterPlot
* HistPlot Frequency
---
## 🛠️ Tech Stack
* Python 🐍
* Jupyter Notebook 📓
* Pandas & NumPy 🧮
* Seaborn & Matplotlib 📈
---
## 🚀 Getting Started
1. Clone the repository:
```bash
git clone https://github.com/OnlineBunker/iris-flower.git
```
2. Launch the notebook:
```bash
jupyter notebook IRIS_flower_classification.ipynb
```
---
## 📊 Dataset Info
The Iris dataset contains 150 samples of iris flowers, with the following features:
* Sepal Length
* Sepal Width
* Petal Length
* Petal Width
* Species (Setosa, Versicolor, Virginica)
---
## ✅ Insights
* Visual relationships between different features
* Distribution patterns by species
* Group-wise feature comparisons
---
## 🤝 Contributing
Suggestions and contributions are welcome! Fork the repo and create a pull request.
---
> "Good data analysis lays the foundation for great machine learning."