Understand the process of cleaning and preprocessing data to ensure its accuracy and reliability. Topics include handling missing data, removing outliers, and standardizing data.
Data Cleaning and Preprocessing 1. Importance of Data Cleaning: Data cleaning is the process of identifying and correcting errors in a dataset to improve its quality and reliability. It is crucial for ensuring that the data is accurate, complete, and consistent, which in turn leads to more reliable analysis and insights. 2. Common Data Cleaning Steps: Handling Missing Values: Decide on strategi...…
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