Secret to Dealing with Missing Data

Have you ever faced the problem of missing data in your data science or machine learning projects?

I’m sure you have, because it’s a very common issue.

And you know what?

It can really mess up your results if you don’t deal with it properly.

That’s why I want to tell you about a cool technique called Predictive Mean Matching (PMM).

It’s a way of filling in the gaps in your data without introducing too much bias.

PMM is like a smart guesser. It looks at the other data you have and tries to figure out what the missing value should be.

But instead of using its own guess, it uses a real value that’s already in the data and that’s close to its guess.

This way, the filled-in values make sense and the shape of your data doesn’t change.

Example: Missing Citizen Data ??

In this dataset, we have three variables?Age,?Income, and Education, but some of the values are missing.

So, we’ll use a tool from sklearn.impute called IterativeImputer. It’s a smart tool that can fill in the missing values by looking at the other variables and using a formula. We’ll use LinearRegression as our formula.

Look at the output. Do you see how the missing values in the Age, Income, and Education columns are gone?

They have been replaced with new values that make sense. These values are not just the average of the column but are guessed values based on the other columns.

This is what PMM does - it uses the connections between variables to make better guesses.

Key Ideas ??

Let me tell you some important things about missing data and how to deal with it.

Missing data is a big problem in data science and machine learning. It can make your results wrong or misleading. So, you need to fix it the right way.

One way to fix it is to use PMM. It’s a technique that guesses the missing values by looking at the other data you have. But it doesn’t use its own guesses. It uses real values that are similar to its guesses.

This is good when your data is not smooth and symmetrical, like when it has outliers or skewness. PMM can keep the shape of your data the same.

But you need to understand what PMM does and how it affects your analysis. You need to look at the new values and how they compare to the old values.

PMM can handle these situations better than other methods, like dropping rows or using the average.

But don’t get me wrong, PMM is not magic.

You still need to understand your data and why some values are missing.

And you need to pay attention to the output of PMM and how it affects your analysis.

So, what do you think?

Are you interested in trying out PMM for your next project? ??