How does data cleaning improve accuracy in predictive data models?

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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 improves accuracy in predictive data models by ensuring that the input data used for analysis is reliable, consistent, and free from errors or irrelevant information. Since predictive models rely heavily on the quality of the data they are trained on, any mistakes—such as duplicates, missing values, inconsistencies, or outliers—can lead to inaccurate predictions and flawed business insights.

The first way data cleaning helps is by removing noise and irrelevant data. Unnecessary or duplicated records can bias results, while irrelevant features may introduce confusion into the model. Cleaning ensures that only meaningful, high-quality data is used.

Second, data cleaning handles missing values appropriately, either by imputing them with statistical techniques (mean, median, mode) or by removing incomplete records. This prevents gaps from distorting model outcomes.

Third, it involves standardizing data formats and correcting errors. For example, if dates are written in multiple formats or categories are misspelled, the model may misinterpret them as different values. Cleaning aligns the data into a consistent format.

Fourth, outlier detection and treatment improve accuracy by preventing extreme, rare values from skewing results. For instance, a single incorrect sales figure entered in millions instead of thousands can heavily distort predictions.

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