import pandas as pd
import numpy as np
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier
from sklearn.metrics import accuracy_score
# Load the Iris dataset
iris = load_iris()
X = iris.data
y = iris.target
# Split the data into training and testing sets (50% test size to reduce training
accuracy)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.5,
random_state=0)
# Initialize the k-NN classifier with k=10 to increase misclassifications
knn = KNeighborsClassifier(n_neighbors=10)
# Train the classifier
knn.fit(X_train, y_train)
# Make predictions on the test set
y_pred = knn.predict(X_test)
# Calculate the accuracy
accuracy = accuracy_score(y_test, y_pred)
print(f'Accuracy: {accuracy:.2f}')
# Print correct and wrong predictions
correct_predictions = []
wrong_predictions = []
for i in range(len(y_test)):
if y_test[i] == y_pred[i]:
correct_predictions.append((X_test[i], y_test[i], y_pred[i]))
else:
wrong_predictions.append((X_test[i], y_test[i], y_pred[i]))
print("\nCorrect Predictions:")
for sample, true_label, predicted_label in correct_predictions:
print(f'Sample: {sample}, True Label: {true_label}, Predicted Label:
{predicted_label}')
print("\nWrong Predictions:")
for sample, true_label, predicted_label in wrong_predictions:
print(f'Sample: {sample}, True Label: {true_label}, Predicted Label:
{predicted_label}')
