What are the encoding techniques you have applied with examples?

• In many practical data science activities, the data set will contain categorical variables.

• These variables are typically stored as text values.

• Since machine learning is based on mathematical equations, it would cause a problem when we keep categorical variables as is.

• Let's consider the following dataset of fruit names and their weights.

Some of the common encoding techniques are:

Label encoding:

• In label encoding, we map each category to a number or a label.

• The labels chosen for the categories have no relationship.

• So, categories that have some ties or are close to each other lose such information after encoding.

One-hot encoding:

• In this method, we map each category to a vector that contains 1 and 0 denoting the presence of the feature or not.

• The number of vectors depends on the categories which we want to keep.

• For high cardinality features, this method produces a lot of columns that slow down learning significantly.