Enrich Missing Data Analysis with Heatmaps

A lesser-known technique to identify feature missingness.

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Missing Data Analysis with Heatmaps

Real-world datasets almost always have missing values.

In most cases, it is unknown to us beforehand why values are missing.

There could be multiple reasons for missing values. Given that we have already covered this in detail in an earlier issue, so here’s a quick recap:

• Missing Completely at Random (MCAR): The value is genuinely missing by itself and has no relation to that or any other observation.

  

• Missing at Random (MAR): Data is missing due to another observed variable. For instance, we may observe that the percentage of missing values differs significantly based on other variables.

• Missing NOT at Random (MNAR): This one is tricky. MNAR occurs when there is a definite pattern in the missing variable. However, it is unrelated to any feature we can observe in our data. In fact, this may depend on an unobserved feature.

Please read this issue for more details: 3 Types of Missing Values.

Identifying the reason for missingness can be extremely useful for further analysis, imputation, and modeling.

Today, let’s understand how we can enrich our missing value analysis with heatmaps.

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Consider we have a daily sales dataset of a store that has the following information:

Store sales dataset

• Day and Date

• Store opening and closing time

• Number of customers

• Total sales

• Account balance at open and close time

We can clearly see some missing values, but the reason is unknown to us.

Here, when doing EDA, many folks compute the column-wise missing frequency as follows:

Column-wise missing value frequency

The above table just highlights the number of missing values in each column.

More specifically, we get to know that:

• Missing values are relatively high in two columns compared to others.

• Missing values in the opening and closing time columns are the same (53).

That’s the only info it provides.

However, the problem with this approach is that it hides many important details about missing values, such as:

• Their specific location in the dataset.

• Periodicity of missing values (if any).

• Missing value correlation across columns, etc.

…which can be extremely useful to understand the reason for missingness.

To put it another way, the above table is more like summary statistics, which rarely depict the true picture.

Why?

We have already discussed this a few times before in this newsletter, such as here and here, and below are the visuals from these posts:

Images from posts on summary statistics

So here’s how I often enrich my missing value analysis with heatmaps.

Compare the missing value table we discussed above with the following heatmap of missing values:

Heatmap of missing values

The white vertical lines depict the location of missing values in a specific column.

Now, it is immediately clear that:

• Values are periodically missing in the opening and closing time columns.

• Missing values are correlated in the opening and closing time columns.

• The missing values in other columns appear to be (not necessarily though) missing completely at random.

Further analysis of the opening time lets us discover that the store always remains closed on Sundays:

Missing values appear on Sundays

Now, we know why the opening and closing times are missing in our dataset.

This information can be beneficial during its imputation.

This specific situation is “Missing at Random (MAR).”

Essentially, as we saw above, the missingness is driven by the value of another observed column.

Now that we know the reason, we can use relevant techniques to impute these values if needed.

For MAR specifically, techniques like kNN imputation, Miss Forest, etc., are quite effective. We covered them here:

MissForest and kNN Imputation for Data Missing at Random

Wasn’t that helpful over naive “missing-value-frequency” analysis?

👉 Over to you: What are some other ways to improve missing data analysis?

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For those wanting to develop “Industry ML” expertise:

At the end of the day, all businesses care about impact. That’s it!

• Can you reduce costs?

• Drive revenue?

• Can you scale ML models?

• Predict trends before they happen?

We have discussed several other topics (with implementations) in the past that align with such topics.

Here are some of them:

• Learn sophisticated graph architectures and how to train them on graph data: A Crash Course on Graph Neural Networks – Part 1

• Learn techniques to run large models on small devices: Quantization: Optimize ML Models to Run Them on Tiny Hardware

• Learn how to generate prediction intervals or sets with strong statistical guarantees for increasing trust: Conformal Predictions: Build Confidence in Your ML Model’s Predictions.

• Learn how to identify causal relationships and answer business questions: A Crash Course on Causality – Part 1

• Learn how to scale ML model training: A Practical Guide to Scaling ML Model Training.

• Learn techniques to reliably roll out new models in production: 5 Must-Know Ways to Test ML Models in Production (Implementation Included)

• Learn how to build privacy-first ML systems: Federated Learning: A Critical Step Towards Privacy-Preserving Machine Learning.

• Learn how to compress ML models and reduce costs: Model Compression: A Critical Step Towards Efficient Machine Learning.

All these resources will help you cultivate key skills that businesses and companies care about the most.

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