PySpark and AWS: Master Big Data with PySpark and AWS - Train and Test Data

The video tutorial explains the process of splitting data into training and test sets, a common practice in AI algorithms like recommender systems. It demonstrates how to use the random split function in Python to divide data into 80% for training and 20% for testing. The tutorial also covers Python notation for data splitting and shows how to count rows in the resulting data frames.

What is the purpose of creating training and test data in a recommender system?

Explain the usual convention for splitting data into training and test sets.

What is the significance of using the test data after training the model?

How does the random split function work in the context of data frames?

What proportion of data is typically used for training and testing?

Describe the process of obtaining the count of rows in a data frame after splitting.

How can you verify the number of rows in the training and test data frames?