
In the field of data science, the adage 'garbage in, garbage out' is often emphasized. This phrase highlights the critical nature of data quality. No matter how sophisticated your machine learning models are, if they are fed poor-quality data, the results will be unreliable and ineffective. Data preprocessing, therefore, plays a pivotal role in ensuring that raw data is transformed into a form suitable for analysis and modeling. Professionals, including those who have completed a Data Science ...
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