Data is the lifeblood of machine learning (ML) projects. At the same time, the data preparation process is one of the main challenges that plague most projects. According to a recent study, data preparation tasks take more than 80% of the time spent on ML projects. Data scientists spend most of their time on data cleaning (25%), labeling (25%), augmentation (15%), aggregation (15%), and identification (5%). Percentage of time allocated to machine learning projects (source)
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