Data Preprocessing
Data preprocessing is a crucial step in data analysis that involves cleaning, transforming, and organizing raw data into a format that is suitable for analysis. It is a fundamental step in data science that helps to improve the quality of data, reduce errors, and enhance the accuracy of the results. Data preprocessing involves several techniques such as data cleaning, data transformation, data integration, data reduction, and data normalization. Data cleaning involves removing or correcting errors, missing values, and inconsistencies in the data. Data transformation involves converting data into a suitable format for analysis, such as scaling, encoding, and feature extraction. Data integration involves combining data from multiple sources into a single dataset. Data reduction involves reducing the size of the dataset by selecting relevant features or samples. Data normalization involves scaling the data to a common range to avoid bias in the analysis.
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