knn

import numpy as np
import pandas as pd
from sklearn import datasets
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.neighbors import KNeighborsClassifier
from sklearn.metrics import accuracy_score, confusion_matrix, classification_report



data = datasets.load_iris()

x = data.data
y = data.target

x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.2, random_state=42)

scaler = StandardScaler()
x_train = scaler.fit_transform(x_train)
x_test = scaler.transform(x_test)

knn = KNeighborsClassifier(n_neighbors=5)
knn.fit(x_train, y_train)

y_pred = knn.predict(x_test)

accuracy = accuracy_score(y_test, y_pred)
confusion = confusion_matrix(y_test, y_pred)
classification_rep = classification_report(y_test, y_pred)

print(f"Accuracy: {accuracy * 100 : .2f}%")
print("Confusion Matrix:\n", confusion)
print("Classification Report:\n", classification_rep)