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1. Explain the importance of data preprocessing in Machine Learning and its impact on model performance.
2. Describe the process of data cleaning and handling missing values in a dataset.
3. Discuss the various methods for dealing with outliers in data preprocessing.
4. Explain the concept of data normalization and its significance in scaling features.
5. Investigate the use of feature scaling techniques like Min-Max scaling and Z-score normalization.
6. Discuss the applications of one-hot encoding and label encoding for categorical variables.
7. Explain the concept of data imputation and its role in replacing missing values.
8. Describe the process of feature selection and its importance in reducing dimensionality.
9. Investigate the use of correlation analysis in data preprocessing to identify relationships between variables.
10. Discuss the significance of data discretization and its applications in handling continuous data.
11. Explain the role of data transformation techniques like log transformation and Box-Cox transformation.
12. Describe the use of binning and bucketing methods for data preprocessing.
13. Investigate the applications of feature engineering in creating new relevant features from existing data.
14. Discuss the process of data standardization and its implications in gradient-based optimization.
15. Explain the use of PCA (Principal Component Analysis) for dimensionality reduction.
16. Describe the significance of handling class imbalance in data preprocessing for classification tasks.
17. Investigate the role of data whitening in data preprocessing and its impact on model convergence.