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What is Data Preprocessing for Regression Analysis about?

This document discusses data preprocessing techniques. It describes data quality issues like missing or noisy data that require cleaning. Major preprocessing tasks include data cleaning, integration, reduction, and transformation. Data cleaning techniques are discussed in depth, such as filling in missing values, identifying outliers, and resolving inconsistencies. Methods for handling missing and noisy data include imputation, binning, regression, and clustering. The document also covers data integration,

Author

Pragati Singh

Language

EN