Building a Customer Churn Prediction Model with Logistic Regression

Nnamdi Nnanna — Mon, 12 Oct 2026 10:52:53 +0000

This week, I worked through Module 3: Machine Learning for Classification in ML Zoomcamp 2026.

The goal was to build a model that could help a telecom company identify customers who were likely to leave. Working through the project helped me connect several machine learning concepts to a practical problem.

What I worked on

Data preparation and exploratory data analysis

I explored the Telco customer churn dataset, examined the target variable, checked missing values, and looked at how different features were distributed.

Feature importance

I learned different ways to understand the relationship between features and customer churn:

Risk ratio and risk difference for categorical variables
Mutual information to measure how informative categorical features are about the target
Correlation coefficients to examine relationships between numerical features

One finding that stood out was that contract type had the highest mutual information among the categorical features we examined.

One-hot encoding with DictVectorizer

Since machine learning models need numerical inputs, I used Scikit-Learn's "DictVectorizer" to convert categorical features into numerical representations. This produced a feature matrix with 45 columns.

Training a logistic regression model

I trained a logistic regression model using Scikit-Learn and achieved approximately 80% accuracy on the validation set.

I also explored how to interpret the model's weights, trained a smaller model using selected features, and worked through how to calculate a prediction manually.

What I learned

The biggest lesson for me was understanding how the different stages of a machine learning project connect.

Feature analysis helps us understand the data. Encoding prepares it for the model. Logistic regression uses those features to estimate the probability of a customer churning, and evaluation helps us understand how well the model performs.

I also learned that the same classification workflow can be applied to other problems, such as spam detection, loan default prediction, and lead scoring.

What's next?

I'm moving on to Module 4: Evaluation Metrics for Classification, where I'll learn more about measuring model performance and understanding what accuracy alone might not tell us.

I'm taking this journey one concept at a time, with the goal of understanding the reasoning behind the code rather than simply getting it to run.

MachineLearning #DataScience #Python #MLZoomcamp #LearningInPublic

DEV Community: Nnamdi Nnanna

Building a Customer Churn Prediction Model with Logistic Regression

MachineLearning #DataScience #Python #MLZoomcamp #LearningInPublic