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Data cleaning significantly impacts model performance by improving data quality, which leads to more accurate and reliable predictions. Removing duplicates, correcting errors, handling missing values, and standardizing formats help reduce noise and bias in the dataset. Clean data enables algorithms to learn meaningful patterns rather than being misled by inconsistencies. This process enhances model accuracy, reduces overfitting, and shortens training time. Without proper data cleaning, even the most sophisticated models may underperform or produce misleading results. Thus, data cleaning is a critical step in the data preprocessing pipeline that directly influences the effectiveness of machine learning models.