https://github.com/mehtadigisha/iris-flower-classification
Iris Flower Classification
https://github.com/mehtadigisha/iris-flower-classification
accuracy-score classification-report data-analysis data-visualization eda iris-classification machine-learning matplotlib pandas prediction python scikit-learn seaborn svc-model svm-model visualization
Last synced: 6 months ago
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Iris Flower Classification
- Host: GitHub
- URL: https://github.com/mehtadigisha/iris-flower-classification
- Owner: mehtadigisha
- Created: 2025-04-14T17:43:05.000Z (6 months ago)
- Default Branch: main
- Last Pushed: 2025-04-14T18:37:50.000Z (6 months ago)
- Last Synced: 2025-04-15T04:15:34.345Z (6 months ago)
- Topics: accuracy-score, classification-report, data-analysis, data-visualization, eda, iris-classification, machine-learning, matplotlib, pandas, prediction, python, scikit-learn, seaborn, svc-model, svm-model, visualization
- Language: Jupyter Notebook
- Homepage:
- Size: 283 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# 🌸 Iris Flower Classification - Machine Learning Project
This project implements a **Supervised Machine Learning** model using **Support Vector Machine (SVM)** to classify iris flowers into three species: *Setosa*, *Versicolor*, and *Virginica*. The model is trained on the classic **Iris dataset**, and includes data visualization, model training, prediction, and evaluation.
---
## 🚀 Features
- 📊 Exploratory Data Analysis with **Seaborn** and **Matplotlib**
- 🤖 SVM (Support Vector Classification) for model building
- 🧪 Model evaluation using **Accuracy Score** and **Classification Report**
- 🔮 Predict the species of Iris flower based on input features
- ✅ Beginner-friendly and well-commented code
---
## 📚 Dataset
The **Iris dataset** contains 150 samples of iris flowers, with the following features:
- `Sepal Length`
- `Sepal Width`
- `Petal Length`
- `Petal Width`
Target classes:
- `Iris-setosa`
- `Iris-versicolor`
- `Iris-virginica`
---
## Libraries Used
- `numpy`
- `pandas`
- `matplotlib`
- `seaborn`
- `scikit-learn`
🧠 Model Training & Evaluation
The model is built using Support Vector Classifier (SVC) from sklearn.svm. It is evaluated using:
✅ Accuracy Score
📄 Classification Report (Precision, Recall, F1-score)
### 💡 How to Use
1. Install the libraries using:
```bash
pip install numpy pandas matplotlib seaborn scikit-learn
```
2. Clone the repository:
``` bash
Copy
Edit
git clone https://github.com/mehtadigisha/Iris-Flower-Classification
```
3. Run the Jupyter Notebook:
``` bash
jupyter notebook iris_classification.ipynb
```
4. Follow the notebook cells to explore the data, train the model, and make predictions.