End to End Machine Learning Project | Customer Churn Prediction in Python

In this video, I build a complete end-to-end Machine Learning project for Customer Churn Prediction using Python.

Customer churn is one of the most important real-world problems in the telecom industry. In this project, we use a Telecom Churn dataset to predict whether a customer is likely to leave the service or stay.

This tutorial is perfect for aspiring Data Scientists, Machine Learning beginners, and Python developers who want to understand how real-world ML projects are built from scratch.

🔍 What you will learn in this video:
✔ Exploratory Data Analysis (EDA) on telecom customer data
✔ Handling imbalanced datasets using SMOTE
✔ Feature engineering using One-Hot Encoding
✔ Training a Random Forest Classifier with high accuracy
✔ Evaluating model performance
✔ Building a live Machine Learning web interface using Gradio

📊 Key features used for churn prediction:

Customer Tenure (Loyalty)
Contract Type (Month-to-Month, One Year, Two Year)
Monthly Charges and Total Charges
Technical Support availability
Online Security status

🛠 Technologies & Libraries Used:

Python
Scikit-Learn
Pandas & NumPy
Random Forest Classifier
SMOTE for class imbalance
Gradio for interactive UI


If you are preparing for Data Science interviews or building your project portfolio, this Customer Churn Prediction project will help you a lot.