Quiz: Supervised Learning,
Unsupervised Learning, Data
Visualization, and Data Preprocessing
Supervised Learning (15 Questions)
1. What distinguishes supervised learning from unsupervised learning?
2. Name two algorithms commonly used for classification tasks.
3. What is the purpose of a training dataset in supervised learning?
4. In supervised learning, what do we call the variable we want to predict?
5. Which metric would you use to evaluate a classification model?
6. True or False: Linear regression can be used for classification tasks.
7. What is overfitting in supervised learning models?
8. Which technique can be used to reduce overfitting?
9. Name a real-world application of supervised learning.
10. What is the difference between classification and regression?
11. In Python, which library provides the train_test_split() function?
12. Code: Write Python code to split a dataset X and y into training and test sets.
13. What does the fit() method do in a supervised learning model?
14. Name one advantage of decision trees over linear models.
15. Code: Implement a simple linear regression model using sklearn.
Unsupervised Learning (10 Questions)
16. What is the main goal of unsupervised learning?
17. Name two popular clustering algorithms.
18. True or False: Unsupervised learning requires labeled data.
19. What is dimensionality reduction? Name one method.
20. Code: Use sklearn to apply KMeans clustering on a dataset.
21. What is the elbow method in clustering?
22. Name a real-world example of unsupervised learning.
23. How does hierarchical clustering differ from KMeans?
24. Code: Implement Principal Component Analysis (PCA) using sklearn.
25. What type of problem would you use market basket analysis for?
Data Visualization (10 Questions)
26. Why is data visualization important in machine learning?
27. Name two Python libraries used for data visualization.
28. Code: Create a scatter plot using Matplotlib.
29. What type of plot is best for visualizing the distribution of a single variable?
30. How do you interpret a heatmap of a correlation matrix?
31. Code: Generate a histogram using Seaborn.
32. Name one advantage of using Seaborn over Matplotlib.
33. What is the purpose of a boxplot?
34. True or False: A line plot is ideal for categorical data.
35. Code: Plot a line graph showing sales over months.
Data Preprocessing (15 Questions)
36. What is the purpose of data preprocessing in machine learning?
37. Name three common data preprocessing tasks.
38. Code: Use SimpleImputer to fill missing values with the mean.
39. Why is feature scaling important?
40. Name two feature scaling techniques.
41. Code: Apply Min-Max scaling to a dataset.
42. What are outliers and how can they affect a model?
43. Name a technique to detect outliers.
44. What is one-hot encoding?
45. Code: Use pandas to perform one-hot encoding on a categorical column.
46. What is label encoding and when should it be used?
47. What is the difference between normalization and standardization?
48. True or False: Data preprocessing should be done after splitting the dataset.
49. Code: Normalize a dataset using sklearn.preprocessing.Normalizer.
50. Why should categorical variables be encoded before model training?
