Understanding Categorical Data

Feature engineering is a crucial step in building a performant machine learning model. Understanding categorical variables and encoding those variables with the right encoding techniques is paramount during the data cleaning and preparation stage. A survey published on Forbes says that Data preparation accounts for about 80% of data scientists’ work. Data scientists spend 60% of their time cleaning and organizing data.

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