46 - Splitting data into training and testing sets for machine learning

When you build a model using machine learning or other means it is important to validate it with a test data set. It is important to test the model on data that the algorithm did not use for training purposes. It is also important for the test data set to follow similar probability distribution as the training data set. The easiest way to achieve this is by splitting the data into training and testing data sets. This video tutorial explains this process in Python using Scikit-learn library.
The code from this video is available at: github.com/bns...