Mastering Data Cleaning: Effective Strategies for Handling Outliers, Missing Data, and More

Data cleaning, also known as data cleansing or data scrubbing, is a crucial step in the data analysis process. It involves detecting and correcting or removing errors, inconsistencies, and inaccuracies from datasets. Despite its importance, data cleaning can present numerous challenges. For the sake of simplicity, we will assume you are using a language like Python with libraries such as pandas, numpy, and sklearn which are common for data cleaning and preprocessing.

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