How to Do Sequence Padding and One-Hot Encoding of Independent Variables
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Condition for Sequence Padding and One-Hot Encoding
Description: Sequence Padding: This technique is used to adjust the length of input sequences to a fixed size by adding padding values (usually zeros) to ensure all sequences are equal in length.
One-Hot Encoding: This technique is used to convert categorical variables into a binary matrix, where each category is represented by a unique column with a 1 for presence and 0 for absence.
Step-by-Step Process
Sequence Padding: Import libraries like keras.preprocessing.sequence to handle sequence padding.
Prepare the Sequences: Create a list of sequences that you want to pad.
Pad Sequences: Use the pad_sequences() function to pad the sequences to a fixed length.
One-Hot Encoding: Use OneHotEncoder from sklearn.preprocessing or pandas.get_dummies() to convert categorical data into a binary matrix.
Sample Source Code
# Code for Padding sequence
from keras.preprocessing.sequence import pad_sequences