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Telecom Customer Churn Prediction using Machine Learning

This project focuses on developing a machine learning system to predict customer churn in the telecommunications industry. It covers the entire data science lifecycle, from exploratory data analysis to model deployment, enabling proactive intervention and customer retention.

29 lectures

What You Will Learn

Mastering the complete machine learning lifecycle for churn prediction
Implementing Exploratory Data Analysis (EDA) to identify key churn drivers
Applying advanced resampling techniques (SMOTE, SMOTEENN) to handle class imbalance
Building and evaluating various machine learning models for churn prediction
Optimizing model performance through systematic hyperparameter tuning using Optuna
Creating interactive web applications using Streamlit for real-time churn prediction
Deploying machine learning applications to the cloud using AWS EC2

System Architecture

Telecom Customer Churn Prediction using Machine Learning Architecture Diagram

High-level architecture overview of the Telecom Customer Churn Prediction using Machine Learning .

What You'll Build

  • A comprehensive EDA report highlighting key churn drivers.
  • A predictive model that accurately identifies customers at risk of churning.
  • An interactive Streamlit web application for predicting churn probability.
  • A deployed machine learning application on AWS EC2, accessible via the web.
Telecom Customer Churn Prediction using Machine Learning
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