This machine learning model predicts the species of an Iris flower based on four key features:
- Sepal Length
- Sepal Width
- Petal Length
- Petal Width
The Iris genus includes over 310 accepted species of flowering plants known for their vibrant and showy blooms. Each Iris flower typically has:
- 3 sepals
- 3 petals
- 3 broad stigma branches (pollen-receptive)
- Hidden anthers beneath the stigma branches
Among these, the dataset focuses on three major species:
| Species Name | Description |
|---|---|
| Iris-setosa | Small petals, easily distinguishable |
| Iris-versicolor | Medium-sized petals |
| Iris-virginica | Largest petals among the three |
| Feature | Description |
|---|---|
| Sepal Length | Length of the flower’s outer part |
| Sepal Width | Width of the flower’s outer part |
| Petal Length | Length of the inner petal |
| Petal Width | Width of the inner petal |
This project uses Logistic Regression, a supervised learning algorithm commonly used for classification tasks. Although originally designed for binary classification, Logistic Regression can be extended to multiclass classification using techniques like one-vs-rest (OvR).
- Simple and effective for linearly separable data
- Fast training and prediction
- Provides probability estimates for each class
- Data Preprocessing: Load and clean the Iris dataset
- Feature Scaling (optional): Normalize input features
- Model Training: Fit Logistic Regression on training data
- Prediction: Predict species based on input features
- Evaluation: Assess accuracy and performance metrics
| Metric | Value |
|---|---|
| Accuracy | 0.9667 |
| Model Type | Multiclass Logistic Regression |
| Classes Predicted | Iris-setosa, Iris-versicolor, Iris-virginica |
The model achieved an impressive 96.67% accuracy, indicating strong performance on the Iris dataset.
- Clone the repository:
git clone https://github.com/yashwanths814/Iris-Model.git cd Iris-Model