What is the role of data cleaning 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.

The role of data cleaning in analysis is to ensure that the data used for decision-making is accurate, consistent, and reliable. Raw data often contains errors, duplicates, missing values, or inconsistencies that can lead to misleading results if not addressed.

Key purposes of data cleaning:

1. Remove errors – Fix typos, formatting issues, or incorrect entries.

2. Handle missing data – Fill in gaps, remove incomplete records, or use imputation methods.

3. Eliminate duplicates – Prevent overcounting or bias in analysis.

4. Ensure consistency – Standardize units, naming conventions, and formats.

5. Improve data quality – Increase accuracy, relevance, and usability.

Why it matters:

• Clean data leads to more accurate models and insights.

• Reduces noise, making patterns easier to detect.

• Prevents bad business decisions caused by faulty information.

In short, data cleaning is like polishing a lens—it ensures you’re looking at a clear, true picture before drawing conclusions. 

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