Build a Customer Churn Prediction Model in Python (Portfolio Project)

A developer published a tutorial demonstrating how to build a binary customer churn prediction model in Python using Pandas and Scikit-Learn, trained on customer demographics, account information, and service usage data. The walkthrough covers environment setup with Jupyter Notebook or VS Code and installing pandas, numpy, scikit-learn, matplotlib, and seaborn, and is accompanied by a YouTube video from the EkamOfficial channel.

Predicting customer churn is one of the most requested skills in Data Science interviews right now. In this tutorial, I'll walk you through how to build a complete Churn Prediction Model from scratch using Python, Pandas, and Scikit-Learn. If you prefer a step-by-step visual walkthrough where I code this live, check out my full YouTube tutorial below: If you find the video helpful, consider subscribing to EkamOfficial https://www.youtube.com/@EkamOfficial-YT for weekly ML and Python projects We are building a binary classification model. Using a dataset of customer demographics, account information, and service usage, our model will predict whether a specific customer is likely to cancel their subscription churn or stay. This is a real-world problem businesses face daily to improve customer retention. First, ensure you have your environment set up. I recommend using Jupyter Notebook or VS Code. Install the required libraries in your terminal: bash pip install pandas numpy scikit-learn matplotlib seaborn