Data cleaning plays a crucial role in improving the accuracy of data science models. Poor-quality data with inconsistencies, missing values, or duplicates can lead to biased or unreliable model predictions. Cleaning the data ensures that the dataset is accurate, consistent, and free from noise, allowing models to learn patterns effectively.

By addressing issues such as outliers, irrelevant features, and incorrect formatting, data cleaning enhances the reliability of insights drawn from the model. It also minimizes errors during the training process, resulting in better performance. For those seeking to excel in data preparation techniques, a data science course is an excellent resource.
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