import sys

# Print all command-line arguments
print("All arguments:", sys.argv)

# Print the noscript name
print("Script name:", sys.argv[0])

# Print the command-line arguments excluding the noscript name
for i in range(1, len(sys.argv)):
print(f"Argument {i}:", sys.argv[i])

If this noscript is executed with command-line arguments like:

python noscript.py arg1 arg2 arg3

The output will be:

All arguments: ['noscript.py', 'arg1', 'arg2', 'arg3']
Script name: noscript.py
Argument 1: arg1
Argument 2: arg2
Argument 3: arg3

class Point:
def init(self, x, y):
self.x = x
self.y = y

def str(self):
return f"Point({self.x}, {self.y})"

# Creating an instance of the Point class
p = Point(3, 4)

# Printing the instance as a string
print(str(p)) # Output: Point(3, 4)

from sklearn.linear_model import LogisticRegression
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score

# Load dataset
data = load_iris()
X = data.data
y = data.target

# Split dataset into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)

# Create a logistic regression model
# Hyperparameters: C (regularization strength) and max_iter (number of iterations)
model = LogisticRegression(C=1.0, max_iter=100)

# Fit the model to the training data
model.fit(X_train, y_train)

# Predict on the test data
y_pred = model.predict(X_test)

# Evaluate the model
accuracy = accuracy_score(y_test, y_pred)
print(f"Model Accuracy: {accuracy:.2f}")

# Display parameters learned by the model
print(f"Model Coefficients: {model.coef_}")
print(f"Model Intercept: {model.intercept_}")