How does data cleaning improve overall model prediction accuracy?

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Supervised and Unsupervised Learning are two primary types of machine learning, differing mainly in hThe primary goal of a data science project is to extract actionable insights from data to support better decision-making, predictions, or automation—ultimately solving a specific business or real-world problem.

Data cleaning plays a vital role in improving overall model prediction accuracy because the quality of the data directly impacts the reliability of the outcomes. In real-world scenarios, datasets often contain missing values, duplicates, inconsistencies, and outliers that can mislead algorithms. If these issues are not handled, the model may learn incorrect patterns, leading to biased or inaccurate predictions. By cleaning the data, unnecessary noise is removed, making the dataset more representative of the actual problem domain.

For example, handling missing values through techniques like imputation prevents the model from discarding useful records. Removing duplicates ensures the model does not overemphasize repeated patterns. Standardizing formats, such as dates and categorical labels, reduces inconsistency and ensures uniformity across the dataset. Outlier detection and treatment minimize distortions in statistical relationships. These processes reduce variance and enhance the model’s ability to generalize well on unseen data.

Moreover, clean data simplifies feature engineering, making it easier to extract meaningful insights and construct relevant predictors. It also shortens training time and reduces computational complexity since irrelevant or redundant data are removed. Ultimately, data cleaning builds a solid foundation for preprocessing, feature selection, and modeling steps, leading to more accurate, consistent, and trustworthy predictions.

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