Hindi Machine Learning Tutorial 6: Master Dummy Variables & One Hot Encoding π
Learn how to effectively handle text data in machine learning by converting categories into numerical format with dummy variables and one hot encoding. Perfect for beginners!

codebasics Hindi
36.1K views β’ Sep 17, 2019

About this video
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/tree/master/ML/5_one_hot_encoding
Exercise csv file: https://github.com/codebasics/py/blob/master/ML/5_one_hot_encoding/Exercise/carprices.csv
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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#MachineLearningHindi #PythonMachineLearning #MachineLearningTutorial #Python #PythonTutorial #PythonTraining #MachineLearningCource #OneHotEncoding
Code in tutorial: https://github.com/codebasics/py/tree/master/ML/5_one_hot_encoding
Exercise csv file: https://github.com/codebasics/py/blob/master/ML/5_one_hot_encoding/Exercise/carprices.csv
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.
Website: http://codebasicshub.com/
Facebook: https://www.facebook.com/codebasicshub
Twitter: https://twitter.com/codebasicshub
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Video Information
Views
36.1K
Likes
646
Duration
21:33
Published
Sep 17, 2019
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4.7
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