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Data quality is paramount in data science and machine learning. The data quality heavily influences machine learningMachine Learning model’s performance. In this context, data cleaning and preprocessingPreprocessing are not just preliminary steps but crucial components of the machine learningMachine Learning pipeline. Data cleaning involves identifying and correcting errors in the dataset, such as dealing with missing or inconsistent data, removing duplicates, and handling outliersOutliers. This chapter will delve into the techniques and best data cleaning and data preprocessingPreprocessing practices. We will cover common techniques and practical tips to improve data science pipeline. This chapter will provide valuable insights to enhance data cleaning and preprocessing skills.

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Practical Aspects in Machine Learning

  • 章节

  • 发表日期：

    2025年

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  • Pramod Gupta,
  • Naresh Kumar Sehgal,
  • John M. Acken

摘要

Data quality is paramount in data science and machine learning. The data quality heavily influences machine learningMachine Learning model’s performance. In this context, data cleaning and preprocessingPreprocessing are not just preliminary steps but crucial components of the machine learningMachine Learning pipeline. Data cleaning involves identifying and correcting errors in the dataset, such as dealing with missing or inconsistent data, removing duplicates, and handling outliersOutliers. This chapter will delve into the techniques and best data cleaning and data preprocessingPreprocessing practices. We will cover common techniques and practical tips to improve data science pipeline. This chapter will provide valuable insights to enhance data cleaning and preprocessing skills.