Can anyone explain one hot encoding

Question

Sklearn in python

Answer 1

One hot encoding is an encoding method for categorical features. Let's say your data has a column containing 3 different values: a, b, and c.

One hot encoding replaces this column with 3 new columns: 1 column for value a, 1 column for value b, one column for value c. The value in the corresponding column will be set to 1, the remaining values will be set to 0.

a -> 100
b -> 010
c -> 001

This is usually necessary for models that are not based on trees, because they do not handle categorical data well. For example k-nearest-neighbors, regression, and support vector machines.

Answer 2

Thanks but how do you implement it in sklearn? step by step code in python