sklearn.model_selection.train_test_split method is used in machine learning projects to split available dataset into training and test set. This way you can train and test on separate datasets. When you test your model using dataset that model didn’t see during training phase, it will give you better idea on the accuracy of a model.
Topics that are covered in this Video:
0:01 – Theory behind why we need to split given dataset into training and test using sklearn train set split method.
0:54 – Coding (Here we use car price prediction problem to demonstrate train test split)
2:14 – Use train_test_split from sklearn
3:54 – Use of random state method
4:49 – Use of fit() method to train your model
5:35 – Score() method (to check the the accuracy of the model)
Machine Learning Tutorial Python – 8: Logistic Regression (Binary Classification): www.youtube.com/watch?v=zM4VZR0px8E&list=PLeo1K3hjS3uvCeTYTeyfe0-rN5r8zn9rw&index=8
To download csv and code for all tutorials: go to 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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