In today's lesson, we delve into the topic of handling missing data - a common occurrence in the realm of data cleaning and manipulation. Regardless of the domain, be it retail, healthcare, finance, or any other, dealing with missing data is a crucial step in maintaining the integrity of the dataset and delivering accurate analyses or predictions.
Dealing with missing values is a cornerstone of the data preprocessing pipeline. Data could be missing in real-life scenarios for various reasons - it might not have been collected, perhaps due to human error or system problems. Regardless of why the data is missing, we need to identify and handle these values to ensure that we make accurate and reliable predictions from our data.
Our first step in handling missing data is to detect those missing values. The Pandas library provides us the isnull() function, which returns a Boolean DataFrame of the same shape as our input, indicating with a True or False whether each individual value is missing.
Using our Titanic dataset as an example, let's demonstrate this process:
