#15: Scikit-learn 12: Preprocessing 12: Categorical: OrdinalEncoder, OneHotEncoder

The video discusses the intuition and code to numerically encode categorical data using OrdinalEncoder() and OneHotEncoder() in Scikit-learn in Python. Timeline (Python 3.8) 00:00 - Outline of video 00:36 - What are categorical features? 01:30 - Why is there a need to encode categorical features? 03:09 - What are ordered categories? 04:09 - Code snippet 05:52 - Open Jupyter notebook 06:06 - Data 06:21 - OrdinalEncoder(): (default) categories='auto' 10:01 - OrdinalEncoder(): categories= -*custom order*- 12:30 - OneHotEncoder(): (default) 16:20 - OneHotEncoder(): specify list of categories 20:35 - OneHotEncoder(): handle_unknown='ignore' 22:27 - OneHotEncoder(): drop='if_binary' 26:25 - DictVectorizer(): one hot encode categories from a dictionary 29:21 - Collinearity: Matrix with determinant = zero 30:42 - Ending notes ######## # Data ######## game = [ ['first', 'gold', 'top'], ['second', 'silver', 'middle'], ['third', 'bronze', 'bottom'] ] x = [ ['football', 'helmet', 'ground'], ['basketball', 'shoes', 'net'], ['cricket', 'bat', 'pitch'], ['tennis', 'band', 'court'] ] h = [ ['male', 'truck', 'blue'], ['female', 'car', 'green'], ['male', 'bike', 'gray'] ] plants = [ {'fruit': 'pear', 'weight': 178.}, {'fruit': 'pomegranate', 'weight': 250.}, {'fruit': 'cherry', 'weight': 5.} ]