knn.py

from sklearn.datasets import load_iris
from sklearn.neighbors import KNeighborsClassifier
from sklearn.preprocessing import StandardScaler
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score, classification_report

# Load the Iris dataset
data = load_iris()
X = data.data
y = data.target
# Split the dataset into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
# Scale the features
scaler = StandardScaler()
X_train = scaler.fit_transform(X_train)
X_test = scaler.transform(X_test)
#Experiment with different values of k 
for k in range(1, 11):
    # Initialize the KNN classifier
    knn = KNeighborsClassifier(n_neighbors=k)
    # Train the classifier
    knn.fit(X_train, y_train)
    # Make predictions
    y_pred = knn.predict(X_test)
    # Evaluate the model
    accuracy = accuracy_score(y_test, y_pred)
    print(f"K={k}: Accuracy={accuracy:.2f}")
    print(classification_report(y_test, y_pred, target_names=data.target_names))