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1. How do you handle missing data in a dataset?

2. Explain the difference between supervised and unsupervised learning.

3. What is cross-validation and why is it important in machine learning?

4. Explain the concept of dimensionality reduction.

5. What is the difference between bagging and boosting in ensemble learning?

6. Explain the Bias-Variance tradeoff.

7. What is the purpose of regularization in linear regression?

8. Explain the ROC curve and its significance in classification models.

9. What is the purpose of A/B testing in data science?

10. Explain the concept of a p-value in hypothesis testing.

11. How do you handle imbalanced datasets in classification problems?

12. Explain the K-Nearest Neighbors (KNN) algorithm.

13. What is the purpose of a confusion matrix in classification problems?

14. Explain the concept of feature engineering.

15. How do you select the appropriate evaluation metric for a regression problem?

16. Explain the concept of natural language processing (NLP) in data science.

17. How do you stay updated with the latest trends and advancements in data science?

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