February 21, 2025 (Updated April 19, 2026)Colin Jaffe/3 min read

Exploring Logistic Regression for Employee Retention Prediction

Retention Model Checklist

0/5

Outcome variable: 1 = stayed, 0 = left (binary classification).

Features: tenure, salary, department, performance reviews, engagement.

Class imbalance handled — turnover often <20%.

Feature scaling for coefficient interpretation.

Test set holdout used to evaluate generalization.

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Introduce logistic regression to predict employee attrition using HR data. Watch this tutorial to learn the key concepts and techniques.

Let's talk about what we're doing next. We have done a Linear Regression predicting continuous values like price. Now, what about discrete values? Dog versus cat—classification problems.

For that, we use Logistic Regression. In this case, we're going to predict whether employees stayed or left their jobs. Given a certain salary, average working hours, department, or whatever features we feed into the model, we want to predict whether the employee will stay or leave. We need a different model—a different type of model—a Logistic Regression.

It's not about drawing a line. It's about answering yes or no—stayed or left. So let's take a look at what code we have.

We are bringing in almost exclusively the same kind of things that we brought in for the last one. StandardScaler, train_test_split. We are bringing in some new metrics.

We're going to dive a bit deeper into how we can best measure our success or failure. How accurate was it by different readings, different measurement tools? And instead of bringing in Linear Regression to create our model, we're bringing in Logistic Regression. All right, make sure you run that and run this, which again, may take a minute if you haven't run it yet, but I already did. Our base URL should be the same.

And now we're grabbing from our CSV some human resources analytics data. We're going to turn that CSV into a DataFrame and call it HR data.

It's what you get when you run pd.read_csv using the base URL we defined above. I'm waiting for this autocomplete to speed up a little bit. There it is.

And the HR CSV URL. And then we can take a look at our HR data, assuming that worked. Here's our data.

We can see quite a lot of columns that can help out. This is their satisfaction level. How well did they perform on their last evaluation? How many projects did they have? What were their average monthly hours? How many years did they spend at the company? How many work accidents have they had? A lot of zeros, that's good.

Did they leave or stay? We have a lot of ones here. One is for left, zero is for stayed. Our first five people all left, our last five people all left.

How many promotions did they receive in the last five years? Well, none—maybe that's why they left. We can see. And what department are they in? Our first five folks are in sales, our last five are in support.

And what is their salary? It is categorized as low, medium, or high. So that's the data we have to work with. We're going to dive into what we'll do with that data in a moment.