Q1. Write a function that takes a list of numbers and returns the mean (average). Then use it to compute mean of [5,10,15,20].
def mean(nums):
return sum(nums) / len(nums) if nums else 0
result = mean([5, 10, 15, 20]) # returns 12.5Q2. In a machine learning pipeline, you need a function to standardize data (subtract mean, divide by standard deviation). Write a function that takes a list and returns standardized list. Use help from statistics module.
import statistics
def standardize(data):
mu = statistics.mean(data)
sigma = statistics.stdev(data)
return [(x - mu) / sigma for x in data]standardize([1,2,3]) yields approximately [-1.224, 0, 1.224]. This is feature scaling.Q3. Write a function that computes mean squared error (MSE) between two lists of predictions and actuals. Use it with y_true = [3, -0.5, 2, 7] and y_pred = [2.5, 0.0, 2, 8].
def mse(y_true, y_pred):
return sum((t-p)**2 for t,p in zip(y_true, y_pred)) / len(y_true)Q4. Write a function using *args to sum any number of arguments. Then write another using **kwargs to print key-value pairs. Demonstrate both.
def sum_all(*args):
return sum(args)
def print_kwargs(**kwargs):
for k, v in kwargs.items():
print(f"{k}: {v}")
print(sum_all(1,2,3,4)) # 10
print_kwargs(name="Alice", age=30)*args collects positional arguments, **kwargs collects keyword arguments.Q5. Create a function that returns a function to add a constant. For example, make_adder(5) returns a function that adds 5 to any number. Then use it to add 5 to 10.
def make_adder(x):
def adder(y):
return x + y
return adder
add5 = make_adder(5)
print(add5(10)) # 15