import hashlib

data ="Hello,world!".encode()
hash_value = hashlib.sha256(data).hexdigest()

print("SHA-256 Hash:", hash_value)

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

iris=load_iris()
X = iris.data
y = iris.target

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)

model = KNeighborsClassifier(n_neighbors=3)
model.fit(X_train, y_train)

y_pred=model.predict(X_test)

accuracy = accuracy_score(y_test, y_pred)
print(f"Accuracy: {accuracy * 100:.2f}%")
print("\nClassification Report:")
print(classification_report(y_test, y_pred, target_names=iris.target_names))

import numpy as np

def initialize_centroids(X, k):
    indices = np.random.choice(len(X), size=k, replace=False)
    return X[indices]

def assign_clusters(X,centroids):
    distances = np.linalg.norm(X[:, np.newaxis] -centroids, axis=2)
    return np.argmin(distances, axis=1)

def update_centroids(X, labels, k):
    return np.array([X[labels== i].mean(axis=0) for i in range(k)])

def has_converged(old_centroids, new_centroids, tolerance=1e-4):
    shifts = np.linalg.norm(new_centroids - old_centroids, axis=1)
    return np.all(shifts < tolerance)

def kmeans(X, k, max_iterations=100):
    centroids=initialize_centroids(X, k)

    for _ in range(max_iterations):
        labels = assign_clusters(X, centroids)
        new_centroids = update_centroids(X, labels, k)

        if has_converged(centroids, new_centroids):
            break

        centroids=new_centroids

    return centroids, labels

import numpy as np
from collections import Counter

def euclidean_distance(a, b):
    return np.sqrt(np.sum((a-b) ** 2))

class KNN:
    def __init__(self, k=3):
        self.k = k

    def fit(self, X, y):
        self.X_train = X
        self.y_train = y

    def predict(self, X):
        return [self._predict(x) for x in X]

    def _predict(self, x):
        
        distances = [euclidean_distance(x, x_train) for x_train in self.X_train]
        
        k_indics = np.argsort(distances)[:self.k]
        
        k_nearest_labels = [self.y_train[i] for i in k_indics]
 
        most_common = Counter(k_nearest_labels).most_common(1)
        return most_common[0][0]