The goal of any data science project is turning raw data into valuable insights for your company.

This means detecting patterns that give accurate predictions, and, consequently, making better decisions.

Let’s look at 6 of the most common problems data scientists face when gathering data to create machine learning models.

Data problem #1: Unrepresentative samples

A key element of any analysis is that the sample should represent what we’re trying to investigate.

If data is collected in a biased way, the results can’t be reliably applied to the broader population.

How to avoid unrepresentative samples:

It’s good to use random sampling whenever possible to test data for bias.

How does data bias happen?

One of the most well-known historical examples of how data bias can fail is the 1936 U.S. presidential election between Alf Landon and Franklin D. Roosevelt.

At the time, The Literary Digest magazine sent a survey to 10 million people. Of those, 2.5 million responded (which was a huge amount of respondents). 

Based on those responses, the magazine predicted Landon would win with nearly 60% of the vote. In reality, Roosevelt won with 62% of the vote.

The issue was in how the data was collected.

First, the magazine selected participants using phone directories. Owning a phone back then already meant you belonged to a certain social class.

The magazine also selected its subscribers, which introduced a strong bias.

In addition to that, participation was voluntary.

They sent the survey and let people choose to respond or not. This added more bias because only politically-engaged people or fans of the magazine answered while other groups were left out.

Data problem #2: Overfitting

Overfitting happens when a model learns not just the underlying patterns in the data but also additional info that may not be so relevant.

Let’s say we’re classifying people into two groups:1) those with heart problems and 2) those without heart problems, and we use factors like blood pressure, weight, and blood sugar to make these classifications.

Our model achieves 90% accuracy. But then, we realize that the model misclassified one person who happened to be wearing blue shoes. To fix this, we add a new rule: 

If someone is wearing blue shoes, then they have heart problems.

This adjustment increases the model’s accuracy to 91%, but it doesn’t improve its ability to extract useful insights.

How to avoid overfitting:

It’s good to use cross-validation (splitting the data set into a training set and a test set) and keep models as simple as possible.

Data problem #3: Underfitting

This is the opposite of overfitting. This is when the model is too simple and fails to capture the real complexity of the data. 

As a result, it performs poorly on both training data and new data because it hasn’t learned enough.

How to avoid underfitting:

It’s good to add meaningful features to your dataset and increase training time or adjust model parameters.

Data problem #4: Missing values

This is something basic but very common: incomplete records.

Some fields are blank or incorrectly entered, and this can affect model training.

There’s no one right answer. It depends on the context and how important the missing data is.

How to avoid missing values:

There’s no one right answer. It depends on the context and how important the missing data is. You can use smart imputation techniques (like mean/mode imputation, KNN or model-based imputation (e.g., MICE)), drop unimportant missing fields and investigate why data is missing.

Data problem #5: Class imbalance

In classification problems (like fraud detection or disease prediction), sometimes one category is much more common than the other.

For example, if 98% of cases are “not fraud” and only 2% are “fraud,” a model that always says “no fraud” will be 98% accurate but essentially useless.

How to avoid class imbalance:

It’s good to use resampling techniques or apply class weighing.

Data problem #6: Insufficient data

Although there is such a thing as having too much data, having too little is also an issue.

We want models to learn general patterns for accurate predictions. With too little data, a model might “learn” things that only apply to that tiny dataset, not the broader reality.

How to avoid insufficient data:

It’s good to augment your data set, use transfer learning or collect more high-quality data before training.

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Working with and analyzing data comes with challenges and complex decisions. In most cases, there’s no single correct way to handle data problems: the best solutions will depend on many factors, including the creativity of the analyst cleaning and preparing the data.

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