Preprocessing
Preprocessing is a crucial step in data science and artificial intelligence that involves cleaning, transforming, and organizing raw data into a format that is suitable for analysis. This step is essential because raw data is often incomplete, inconsistent, or contains errors that can lead to inaccurate results. Preprocessing involves several techniques such as data cleaning, data transformation, data normalization, and data integration. Data cleaning involves removing or correcting errors, missing values, and inconsistencies in the data. Data transformation involves converting data from one format to another, such as converting categorical data to numerical data. Data normalization involves scaling the data to a common range to avoid bias in the analysis. Data integration involves combining data from multiple sources into a single dataset. Preprocessing is a critical step in data science and artificial intelligence because it can significantly impact the accuracy and reliability of the results obtained from the analysis.
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