Handling missing values is a critical step in data preprocessing. The way missing values are treated can significantly impact the outcomes of data analysis and machine learning models. This notebook outlines different methods for dealing with missing data, providing guidance on when to use each approach. Objectives
1. Identify and visualize missing data.
2. Explore different methods to handle missing values.
3. Understand the implications of each method.
4. Implement the methods using Python (pandas).