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 for weekly ML and Python projects!)
🛠️ What We Are Building
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.
1. Prerequisites & Setup
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
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