✅ Data Science Interview Questions with Answers Part-8

71. What is clustering?
Clustering is an unsupervised learning technique that groups similar data points together based on distance or similarity. It is used to discover natural segments in data without predefined labels.

72. Difference between K-means and hierarchical clustering?
K-means requires the number of clusters to be defined in advance and works well for large datasets. Hierarchical clustering builds a tree of clusters without needing a predefined number but is computationally expensive for large data.

73. How do you choose value of K?
The value of K is chosen using methods like the elbow method, silhouette score, or domain knowledge. The goal is to balance compact clusters with meaningful separation.

74. What is PCA?
Principal Component Analysis is a dimensionality reduction technique that transforms correlated features into a smaller set of uncorrelated components while retaining maximum variance.

75. Why is dimensionality reduction needed?
Dimensionality reduction reduces noise, improves model performance, lowers computation cost, and helps visualize high-dimensional data.

76. What is anomaly detection?
Anomaly detection identifies rare or unusual data points that deviate significantly from normal patterns. It is commonly used in fraud detection, network security, and quality monitoring.

77. What is association rule mining?
Association rule mining discovers relationships between items in large datasets. It is widely used in market basket analysis to identify product combinations that occur together.

78. What is DBSCAN?
DBSCAN is a density-based clustering algorithm that groups closely packed points and identifies noise. It works well for clusters of arbitrary shape and handles outliers effectively.

79. What is cosine similarity?
Cosine similarity measures the angle between two vectors to assess similarity. It is commonly used in text analysis and recommendation systems where magnitude is less important.

80. Where is unsupervised learning used?
Unsupervised learning is used in customer segmentation, recommendation systems, anomaly detection, topic modeling, and exploratory analysis where labeled data is unavailable.

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