Label Encoding vs One hot Encoding Categorical Data Machine Learning | Feature Engineering Part 13

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One-Hot Encoding is the process of creating dummy variables. In this encoding technique, each category is represented as a one-hot vector.
In machine learning, we usually deal with datasets which contains multiple labels. These labels can be in the form of words or numbers. To make the data understandable or in human readable form, the training data is often labeled in words.
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Good explanation, keep making more videos

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