Module 4 (Step-by-Step Implementation)

In this module, we focus entirely on implementing the Customer Churn Prediction project in Google Colab. The aim is to take the raw telecom customer dataset and process it step by step, so that beginners can clearly understand how real-world data is transformed into a structured machine learning solution.

This module emphasizes practical execution. We begin with preparing the dataset and setting up the environment, then move through data cleaning, feature engineering, and encoding. Finally, we build and evaluate a Logistic Regression model to predict customer churn.

Each lesson builds logically on the previous one to ensure a smooth and structured learning experience. By following this module, learners will see how raw customer data is converted into a complete churn prediction pipeline ready for business decision-making.