This project uses classic machine learning classifiers to identify Iris flower species based on sepal and petal measurements from the well-known Iris dataset (scikit-learn built-in). It compares the performance of four supervised algorithms.
To train and evaluate models that classify iris flowers into three species—setosa, versicolor, and virginica—based on flower measurements.
- Decision Tree
- Support Vector Machine (SVM)
- K-Nearest Neighbors (KNN)
- Naive Bayes
- Load and inspect the dataset
- Split data into training and test sets
- Apply feature scaling when needed
- Train each model
- Generate predictions and evaluation metrics
- Visualize results with confusion matrices
- Classification report (precision, recall, f1-score)
- Accuracy
- Confusion matrix (via heatmap)
Iris_Flowers.ipynb— main notebookIris_Flowers.pdf— exported version for non-technical viewers
- Python 3
- scikit-learn
- pandas, NumPy
- seaborn, matplotlib
This project is released under the MIT License.