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import numpy as np
import matplotlib.pyplot as plt
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, classification_report, confusion_matrix
# Step 1: Load the dataset
iris = load_iris()
X = iris.data # Features
y = iris.target # Labels
# Step 2: Split the dataset
X_train, X_test, y_train, y_test = train_test_split(
X, y, test_size=0.2, random_state=42
)
# Step 3: Create and train the KNN classifier
knn_classifier = KNeighborsClassifier(n_neighbors=5)
knn_classifier.fit(X_train, y_train)
# Step 4: Make predictions
y_pred = knn_classifier.predict(X_test)
# Step 5: Evaluate the model
accuracy = accuracy_score(y_test, y_pred)
print(f'Accuracy: {accuracy:.4f}')
print('\nClassification Report:')
print(classification_report(y_test, y_pred, target_names=iris.target_names))
print('\nConfusion Matrix:')
print(confusion_matrix(y_test, y_pred))
# Step 6: Predict a new input sample
input_sample = np.array([[5.1, 3.5, 1.4, 0.2]])
predicted_class = knn_classifier.predict(input_sample)
predicted_prob = knn_classifier.predict_proba(input_sample)
print("\nInput Sample:", input_sample)
print("Predicted Flower Name:", iris.target_names[predicted_class[0]])
print("Prediction Probabilities:", predicted_prob)
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