Data preprocessing is the first step in machine learning to ensure data quality and extract useful information from datasets. Derived data after data processing is used for model training and has a direct impact on model efficiency. If there is no relevant and dispensable information in the dataset, it will be removed from the dataset to ensure data quality. Data pre-processing includes description of data, null value handling, categorical value coding, normalization, transformation, extraction and selection of various features.
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Cite this article
Chithra Y, Prathibha Kiran, P B Manoj, “The Novel Method for Data Preprocessing CLI”, Advances in Intelligent Systems and Technologies, pp. 117-120, December. 2022. doi: 10.53759/aist/978-9914-9946-1-2_21
