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.

Data preprocessing in machine learning ensures data quality and extracts useful information from datasets, impacting model efficiency and ensuring data quality for machine learning models.

Paper

The Novel Method for Data Preprocessing CLI

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