Python Machine Learning Tutorial: Mastering Dummy Variables & One Hot Encoding 🧠

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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Next Video:
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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To download csv and code for all tutorials: go to https://github.com/codebasics/py, click on a green button to clone or download the entire repository and then go to relevant folder to get access to that specific file.

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