A churn model is more useful when it can actually be used.

I recently built a Customer Churn Prediction application that takes the workflow beyond a Jupyter Notebook and turns it into an interactive ML application.

The workflow: Raw customer data → preprocessing → feature preparation → ML model → churn probability → business interpretation → Streamlit application

A few things I focused on:

• Preparing customer, service, contract, and billing data correctly • Building and evaluating a classification model • Returning prediction probability instead of only a class label • Making the output understandable through business-oriented interpretation • Integrating the trained model into a usable Streamlit application

One lesson from the project: a model score is only one part of an ML solution. The real value comes from connecting the model to a workflow that someone can actually use.

I’m currently building more projects around Machine Learning, Data Science, AI applications, and deployment.

What part of an ML project do you think is most often overlooked: data preparation, model evaluation, or deployment?