Handling Missing Data in Machine Learning

When working with data for machine learning, dealing with missing information is really important. Here are some simple ways to identify and fix missing data:

1. Finding Missing Data:

   • You can use a tool called isnull() in a program called pandas to check for missing values. Here’s how you can do it:

     df.isnull().sum()
     

   • You can also use pictures, like heatmaps, from a library called Seaborn to see where the missing data is quickly.

2. Fixing Missing Data:

   • Deletion: If only a few pieces of information are missing, you can just remove those rows or columns.
   • Imputation: This means filling in the missing values:
     • Use the average (mean) or middle number (median) for numbers.
     • Use the most common value (mode) for categories.
     • For a more advanced method, you can use models like KNN to fill in the gaps.

3. Example: Let’s say you have a list of people, and some ages are missing. You could fill in those missing ages by using the average age of the people you do have. This keeps your data strong and ready for analysis.