ML Project Pipeline Part 5: Check Missing Values in Numerical Data ⚠️
In this Hindi tutorial, Akansha Yadav explains why detecting missing (null/blank) values in a dataset is a prerequisite for any meaningful analysis. She demonstrates how pandas' `isnull()` function returns a Boolean mask (True for missing entries, False otherwise) and how combining it with `sum()` converts that mask into concrete counts of missing rows or columns. The walkthrough covers both column‑wise and row‑wise detection, showing how to interpret the resulting tables and why summing the Boolean values yields the total number of empty cells per feature. The video emphasizes that without handling these nulls, downstream ML modeling would be unreliable, and it provides a clear, step‑by‑step method to quantify and locate missing data before proceeding with the rest of the pipeline.
