Pandas profiling is a step to find the effective number of usable data. It gives us the statistics of NULL values and the usable values and thus makes variable selection and data selection for building models in the preprocessing phase very effective.

In a machine learning interview, if you’re asked about Pandas Profiling, you can provide the following answer:

Pandas Profiling is a Python library that is used for exploratory data analysis (EDA) of a DataFrame. It generates a comprehensive report with information about the distribution of data, missing values, summary statistics, and correlation among variables. The report is presented in an interactive HTML format, making it easy for data scientists and analysts to quickly understand the characteristics of the dataset. Pandas Profiling automates many of the routine tasks involved in the initial stages of data exploration, allowing for a faster and more efficient analysis of the data. It is a valuable tool for understanding the structure and content of a dataset before diving into more advanced data preprocessing and model building stages in machine learning projects.