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What is Data Preprocessing Techniques Explained about?

The document outlines the importance of data preprocessing in data analysis and machine learning, emphasizing steps such as data cleaning, transformation, feature selection, integration, and reduction. It details techniques for handling missing values, outliers, and duplicates, as well as methods for normalization, standardization, and dimensionality reduction. The conclusion highlights that effective preprocessing enhances model accuracy and efficiency, leading to better insights and decision-making.

Author

Rishit Goel

Language

EN