Exploratory data analysis (EDA) is a common task performed by business analysts to discover patterns, understand relationships, validate assumptions, and identify anomalies in their data. In machine learning (ML), it’s important to first understand the data and its relationships before getting into model building. Traditional ML development cycles can sometimes take months and require advanced data science and ML engineering skills, whereas no-code ML solutions can help companies accelerate the delivery of ML solutions to days or even hours.
Amazon SageMaker Canvas is a no-code ML tool that helps business analysts generate accurate ML predictions without having to write code or without requiring any ML experience. Canvas provides an easy-to-use visual interface to load, cleanse, and transform the datasets, followed by building ML models and generating accurate predictions.
In this post, we walk through how to perform EDA to gain a better understanding of your data before building your ML

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