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The Lifecycle of Feature Engineering: From Raw Data to Model-Ready Inputs

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Understanding Raw Data Raw data contains inconsistencies, noise, missing values, and irrelevant details. Understanding the nature, format, and quality of raw data is the first step in feature engineering. Data audit : Identify variable types (e.g., AutoML frameworks : Tools like Google AutoML and H2O.ai

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When his hobbies went on hiatus, this Kaggler made fighting COVID-19 with data his mission | A…

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David: My technical background is in ETL, data extraction, data engineering and data analytics. I spent over a decade of my career developing large-scale data pipelines to transform both structured and unstructured data into formats that can be utilized in downstream systems.

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