Data Wrangling

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Data wrangling, also known as data preprocessing, is the process of cleaning and preparing raw data for analysis or use in machine learning. Raw data often comes in messy formats, missing values, or with errors, and data wrangling involves handling these issues to transform the data into a consistent and usable format.

This step is essential because poorly wrangled data can lead to inaccurate predictions or improper learning by the AI model. Data wrangling may involve tasks such as converting text to numbers, normalizing values, handling missing data, or splitting data into training and test sets. It’s one of those behind-the-scenes tasks that can take up a lot of time but is vital for ensuring that the AI is provided with high-quality data.

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