Handling Missing Values in Data with Python | Machine Learning
In real-world scenarios, we collect data from different sources, and inconsistency in data is a very general problem. In machine learning or data science, we call it noise in data. As we know while building a model if garbage is in then garbage is out. Missing value in any dataset is also a kind of noise. There are multiple ways of handling missing values in data with Python (
#machinelearning). In this video, we will explore all of them.
