Data Cleaning is the process of detecting and correcting (or removing) corrupt or inaccurate records from a dataset. This step is crucial for ensuring the quality and reliability of the data used in analysis or modeling. Data cleaning involves tasks such as removing duplicates, filling in missing values, correcting errors, and standardizing formats. It helps to eliminate noise and inconsistencies that can distort insights and predictions. Effective data cleaning improves data integrity, making it easier to derive accurate and actionable insights from the dataset.