Q1. Scenario: You have a list of model accuracies [0.85, 0.92, 0.78, 0.95, 0.88]. Write a loop to find the highest accuracy and print it. Also, count how many models have accuracy above 0.90.
max_acc = max(accuracies). Count: sum(1 for acc in accuracies if acc > 0.90). Using loop: highest = accuracies[0]; for acc in accuracies: if acc > highest: highest = acc. Conditional statements (if) inside loop. This is basic iteration and comparison.
Q2. Scenario: A dataset has missing values marked as -999. Filter out these values and compute the mean of remaining numbers using a loop and conditional.
data = [10, -999, 20, 30, -999, 40]; filtered = [x for x in data if x != -999]; mean = sum(filtered)/len(filtered). Using loop: total=0; count=0; for x in data: if x != -999: total+=x; count+=1. mean = total/count. This simulates data cleaning.
Q3. Scenario: You need to classify a person's BMI category: underweight (<18.5), normal (18.5-24.9), overweight (25-29.9), obese (>=30). Write a conditional statement for BMI=23.7.
if bmi < 18.5: category = "Underweight"; elif bmi < 25: category = "Normal"; elif bmi < 30: category = "Overweight"; else: category = "Obese". For bmi=23.7 -> "Normal". Boolean conditions and elif chains are fundamental for rule-based classification.
Q4. Scenario: You are training a model. You have a list of epoch numbers [1,2,3,4,5] and corresponding loss values [0.5,0.4,0.35,0.33,0.32]. Write a loop to print each epoch and its loss. Stop the loop if loss drops below 0.34 using break.
for epoch, loss in zip(epochs, losses): print(f"Epoch {epoch}: loss {loss}"); if loss < 0.34: break. This will print first 4 epochs. Break exits the loop prematurely. This is used in early stopping during training.
Q5. Scenario: Generate a list of even numbers from 0 to 20 using a for loop with range and a conditional. Then create the same list using list comprehension.
Loop: evens = []; for i in range(21): if i % 2 == 0: evens.append(i). List comprehension: evens = [i for i in range(21) if i%2==0]. Both produce [0,2,4,...,20]. Comprehension is more Pythonic and efficient.