import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier
from sklearn.metrics import accuracy_score, confusion_matrix, classification_report
# Sample data
data = {
 'Feature1': [2, 4, 4, 4, 6, 6, 6, 8, 8, 8],
 'Feature2': [4, 2, 4, 6, 2, 4, 6, 2, 4, 6],
 'Target': [0, 0, 0, 0, 1, 1, 1, 1, 1, 1]
}
df = pd.DataFrame(data)
# Split data into features and target
X = df[['Feature1', 'Feature2']]
y = df['Target']
# Split dataset
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)
# Initialize and fit model
knn = KNeighborsClassifier(n_neighbors=3)
knn.fit(X_train, y_train)
# Make predictions
y_pred = knn.predict(X_test)
# Evaluate model
accuracy = accuracy_score(y_test, y_pred)
conf_matrix = confusion_matrix(y_test, y_pred)
class_report = classification_report(y_test, y_pred)