Review:
Data Preprocessing Techniques
overall review score: 4.5
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score is between 0 and 5
Data preprocessing techniques refer to the various methods and processes used to clean, transform, and prepare raw data for analysis or machine learning models.
Key Features
- Cleaning and formatting data
- Handling missing values
- Normalization and standardization
- Feature engineering
- Dimensionality reduction
Pros
- Improves data quality and accuracy
- Enhances model performance
- Helps in identifying patterns and trends in data
Cons
- Can be time-consuming, especially for large datasets
- Requires domain knowledge to choose appropriate techniques