Machine Learning Tutorial Python – 6: Dummy Variables & One Hot Encoding
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Machine learning models work very well for dataset having only numbers. But how do we handle text information in dataset? Simple approach is to use interger or label encoding but when categorical variables are nominal, using simple label encoding can be problematic. One hot encoding is the technique that can help in this situation. In this tutorial, we will use pandas get_dummies method to create dummy variables that allows us to perform one hot encoding on given dataset. Alternatively we can use sklearn.preprocessing OneHotEncoder as well to create dummy variables.
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Code in tutorial: https://github.com/codebasics/py/blob/master/ML/5_one_hot_encoding/one_hot_encoding.ipynb
Exercise csv file: https://github.com/codebasics/py/blob/master/ML/5_one_hot_encoding/Exercise/carprices.csv
Exercise solution: https://github.com/codebasics/py/blob/master/ML/5_one_hot_encoding/Exercise/exercise_one_hot_encoding.ipynb
Topics that are covered in this Video:
0:00 Introduction
0:47 How to handle text data in machine learning model?
1:38 Nominal vs Ordinal Variables
2:44 Theory (Explain one hot encoding using home prices in different townships)
3:39 Coding (Start)
3:51 Pandas get_dummies method
7:48 Create a model that uses dummy columns
12:45 Label Encoder
13:29 fit_transform() method
15:40 sklearn OneHotEncoder
19:59 Exercise (To predict prices of car based on car model, age, mileage)
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Machine Learning Tutorial Python – 7: Training and Testing Data: https://www.youtube.com/watch?v=fwY9Qv96DJY&list=PLeo1K3hjS3uvCeTYTeyfe0-rN5r8zn9rw&index=7
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