Data Auditing and Initial Inspection

Before moving forward, it is essential to inspect the data to ensure that everything has loaded correctly and that there are no structural issues. Data auditing helps identify problems early, such as missing values, incorrect data types, or unexpected columns.

To begin this inspection, we view the first few rows of each dataset.

Code:

movies.head()

ratings.head()

This allows us to visually confirm that movie titles, genres, user IDs, and ratings appear as expected. It also helps verify that the dataset structure matches the problem we are trying to solve.

To further validate the datasets, we examine detailed information about each DataFrame.

Code:

print(movies.info())

print(ratings.info())

From this output, we can confirm column names, data types, and the presence or absence of missing values. This step gives confidence that the dataset is clean enough to proceed and that no immediate preprocessing fixes are required.