Portfolio
Blog Post Artifact

Handling Real-World Machine Learning Data: 3 Practical Challenges

In machine learning, models depend on good data. In practice, data is often incomplete or incorrect. Here is how to solve three common data challenges engineers face in production.

Challenge 1: Missing Data

The Scenario: Imagine building an app to predict student test scores. Some students forgot to log their study hours.

The Approach: It is tempting to just delete the rows with missing data or fill every blank spot with the class average. Both options cause problems. If only the students who skip class forgot to log their study hours, filling their missing data with the class average ignores that connection and creates a false picture.

Before altering the dataset, I run statistical checks to see if the missing data follows a pattern.

The Solution Instead of using a simple average, I use K-Nearest Neighbors (KNN) imputation. This means looking at similar data points. If a student with missing data has the same attendance and past grades as three other students, we borrow their average study hours. This keeps the dataset realistic.

Challenge 2: Incorrect Labels

The Scenario: Consider a medical image model trained to spot anomalies, or a spam filter where users accidentally click "Spam" on important emails. These incorrect labels confuse the model during training.

The Approach: You cannot manually review millions of images or emails to fix human errors. You need a system to find the mistakes automatically.

The Solution I use the model to find its own bad data.

Targeted Review: After an initial training run, I isolate the top 5% of cases where the model is highly confident in its prediction, but that prediction disagrees with the assigned label.

Expert Routing: I route only those flagged, high-priority cases to human experts for secure review.

Scaling Up: If the dataset is too large for any manual review, I use active learning to focus strictly on the most uncertain data points.

Challenge 3: Too Many Features (High Dimensionality)

The Scenario: Imagine predicting credit card fraud. You have useful data like the transaction amount and the store location. But you also have thousands of useless data points, like the exact millisecond of the purchase or the font size of the user's browser. Feeding all this to a model makes it slow and prone to memorizing the training data instead of learning actual patterns.

The Approach: We need to remove the noise without losing the signal. High dimensionality requires methodical pruning.

The Solution Feature Engineering: I group related data to make it useful. For example, converting exact transaction times into simple categories like "Morning" or "Evening."

Feature Selection: I use statistical tests to drop features that have no mathematical link to fraud.

Preventing Leakage: The most important rule here is preventing data leakage. I make sure to calculate feature selections only using the training data during cross-validation. Applying global changes before splitting the data gives the model answers it should not have.

Summary

Building the model is only part of the job. Handling missing values, fixing bad labels, and reducing unnecessary data are the steps that actually make a model work reliably in production.