How does data cleaning improve accuracy in analysis?

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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 is one of the most critical steps in data science because the quality of insights largely depends on the quality of data being analyzed. In real-world scenarios, raw data often contains errors such as duplicates, missing values, outliers, inconsistent formats, and irrelevant information. If such issues are not addressed, they can lead to misleading results, poor model performance, and inaccurate conclusions. By applying data cleaning techniques, analysts ensure that the dataset is consistent, reliable, and relevant for the intended analysis.

For example, handling missing values prevents bias in statistical results, while removing duplicates ensures that outcomes are not skewed by repeated records. Standardizing formats, such as dates or categorical variables, makes integration with other datasets easier and improves comparability. Outlier detection and treatment further help in reducing distortions in predictive modeling. Cleaned data not only increases the accuracy of statistical analysis but also enhances the performance of machine learning models by reducing noise and irrelevant patterns.

Moreover, data cleaning builds trust in the results, which is crucial for decision-making in businesses, healthcare, finance, and other fields. Without this step, even advanced algorithms cannot deliver meaningful insights. Thus, data cleaning directly contributes to accuracy, efficiency, and reliability, making it a foundation for any successful data-driven project.

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