Branch-AI/one_exercise_per_file/week02/day03/ex03/readme.md

Exercise 3 One hot Encoder

The goal of this exercise is to learn how to deal with Categorical variables using the OneHot Encoder.

X_train = [['Python'], ['Java'], ['Java'], ['C++']]

1. Using OneHotEncoder with handle_unknown='ignore', fit the One Hot Encoder and transform X_train. The expected output is:

   	('C++',)	('Java',)	('Python',)
   0	0	0	1
   1	0	1	0
   2	0	1	0
   3	1	0	0

   To get this output create a DataFrame from the transformed X_train and the attribute categories_.

2. Transform X_test using the fitted One Hot Encoder on the train set.

X_test = [['Python'], ['Java'], ['C'], ['C++']]

The expected output is:

|    |   ('C++',) |   ('Java',) |   ('Python',) |
|---:|-----------:|------------:|--------------:|
|  0 |          0 |           0 |             1 |
|  1 |          0 |           1 |             0 |
|  2 |          0 |           0 |             0 |
|  3 |          1 |           0 |             0 |

• https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.OneHotEncoder.html