Iris Flower Classification

Questions to Answer

The Iris dataset is one of the most famous datasets in machine learning, introduced by statistician Ronald Fisher in 1936. Download the CSV file or load it directly from scikit-learn:

Features Overview

Sample Data Preview

Create a single well-organized Jupyter notebook that covers all the following components with clear documentation and visualizations.

Train and compare the following classifiers. Use default parameters first, then optionally tune hyperparameters for your best performer.

Evaluation Metrics

Sample Code

# Load and prepare data
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler

iris = load_iris()
X, y = iris.data, iris.target

X_train, X_test, y_train, y_test = train_test_split(
    X, y, test_size=0.2, random_state=42, stratify=y
)

scaler = StandardScaler()
X_train_scaled = scaler.fit_transform(X_train)
X_test_scaled = scaler.transform(X_test)

# Train classifiers
from sklearn.linear_model import LogisticRegression
from sklearn.neighbors import KNeighborsClassifier
from sklearn.svm import SVC
from sklearn.tree import DecisionTreeClassifier

models = {
    'Logistic Regression': LogisticRegression(max_iter=200),
    'KNN (k=5)': KNeighborsClassifier(n_neighbors=5),
    'SVM (RBF)': SVC(kernel='rbf', gamma='scale'),
    'Decision Tree': DecisionTreeClassifier(max_depth=4, random_state=42)
}

for name, model in models.items():
    model.fit(X_train_scaled, y_train)
    accuracy = model.score(X_test_scaled, y_test)
    print(f'{name}: {accuracy:.4f}')

Create at least 10 visualizations in your notebook. Each should have proper titles, labels, and brief interpretive commentary.

Create a public GitHub repository with the exact name shown below:

Required Project Structure

iris-classification-ml/
├── data/
│   └── iris.csv                  # Dataset (or note if using sklearn)
├── notebooks/
│   └── iris_classification.ipynb # Main analysis notebook
├── visualizations/
│   ├── pair_plot.png
│   ├── confusion_matrices.png
│   ├── model_comparison.png
│   └── decision_boundaries.png
├── requirements.txt              # Python dependencies
└── README.md                     # Project documentation

Your project will be graded on the following criteria. Total: 200 points.