One-Hot Encoding is a method to encode categorical variables to numerical data that Machine Learning algorithms can deal with. One-Hot encoding is most used during feature engineering for a ML Model. It converts categorical values into a new categorical column and assign a binary value of 1 or 0 to those columns.
Also known as Dummy Encoding, One-Hot Encoding is a method to encode categorical variables, where no such ordinal relationship exists, to numerical data that Machine Learning algorithms can deal with. One hot encoding is the most widespread approach, and it works very well unless your categorical variable takes on a large number of unique values. One hot encoding creates new, binary columns, indicating the presence of each possible value from the original data. These columns store ones and zeros for each row, indicating the categorical value of that row.
