Hit > Play to see the second video in this series on Exploratory Data Analysis
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In this video, the second in a series on Exploratory Data Analysis, we continue with our data pipeline development and examine non-numeric, categorical data.
To ensure that our data is well formed to train our AI and ML models we need to develop a strategy for dealing with missing data We will also need to convert our text-based categorical data into a numeric representation suitable for our models.
In this video we look at the different strategies for dealing with nominal and ordinal data. In so doing we encounter multicollinearity and the 'Dummy Data Trap'
