answer:def faster_sum(arr): total = 0 # looping over the array and adding the elements # to the variable total for i in range(0, len(arr)): total += arr[i] return total list_of_numbers = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10] # using the faster_sum() function result = faster_sum(list_of_numbers) print(result)

answer:import string import random def generate_password(): chars = string.ascii_uppercase + string.ascii_lowercase + string.digits size = 8 return ''.join(random.choice(chars) for x in range(size,15)) password = generate_password() print("The generated random 8-character alphanumeric password is: " + password)

answer:# import the necessary libraries import pandas as pd import numpy as np from sklearn.ensemble import IsolationForest from sklearn.model_selection import train_test_split # Read the dataset data = pd.read_csv('dataset.csv') # Split the dataset into train and test datasets x_train, x_test, y_train, y_test = train_test_split(data.iloc[:,:-1], data.iloc[:,-1], test_size=0.2) # Create the isolation forest model model = IsolationForest() model = model.fit(x_train, y_train) # Make predictions y_pred = model.predict(x_test) # Evaluate the model performance from sklearn.metrics import accuracy_score print('Accuracy:', accuracy_score(y_test, y_pred))

answer:import sys def main(): if len(sys.argv) < 3: print('Error: not enough arguments') return try: num1 = int(sys.argv[1]) num2 = int(sys.argv[2]) except ValueError: print('Error: arguments should be numbers') return print(num1 + num2) if __name__ == '__main__': main()