Practical Guide to Telecom Customer Churn Prediction: End-to-End Machine Learning Project Analysis Using LightGBM and Deep Learning

This project is a practical customer churn prediction initiative for Interconnect Telecom Company, published by audemx on GitHub (Project link: https://github.com/audemx/Customer-Churn-Prediction-in-Interconnect, published date: 2026-06-06). It covers the complete machine learning workflow from data engineering to model optimization, using LightGBM and deep learning methods. The core goal is to achieve the business metric of AUC-ROC ≥ 0.88, helping the marketing team implement targeted retention campaigns through promo codes, financial incentives, and personalized loyalty programs.

Customer churn is a core challenge in the telecom industry—enterprises lose revenue when customers switch to competitors and bear high customer acquisition costs. This project builds a predictive analytics system for Interconnect, with strategic goals including: primary success metric AUC-ROC ≥0.88 (threshold for production deployment), secondary metric classification accuracy, and business value of supporting the marketing team to implement targeted retention strategies.

Data Integration

The project integrates four types of data sources using customer ID as the key: contract data (type, term, etc.), personal information (demographic features), internet service (type and usage), and phone service (call details).

Data Quality

• No duplicate records; service subscription imbalance (5517 users have internet service, 6361 have phone service)
• Target variable distribution: 73.46% active customers, 26.54% churned customers (class imbalance dictates prioritizing AUC-ROC for evaluation)

Data Cleaning

• Column names converted to lowercase snake_case
• total_charges field converted from string to float64
• 11 missing total charge records for new customers filled with 0.0

Six-Stage Pipeline

1. Infrastructure: Create a conda virtual environment and install dependencies such as scikit-learn, LightGBM, TensorFlow (including Metal acceleration plugin)
2. EDA: Analyze feature distribution and correlation
3. Feature Engineering: Extract churn labels and build customer lifecycle features
4. Data Preparation: Prevent data leakage, encode categorical variables, split dataset using 80/20 stratified sampling
5. Model Training: Test LightGBM (hyperparameter optimization), TensorFlow MLP (parameter tuning with Keras Tuner), and baseline models like logistic regression and random forest
6. Evaluation: Independent test set validation, ROC curve analysis, feature importance interpretation, and business value conversion

For the 26.54% churn rate, the project uses multiple strategies:

1. Prioritize AUC-ROC as the evaluation metric (to avoid accuracy misleading)
2. Use stratified sampling during data splitting to maintain class proportions
3. At the model level: LightGBM uses class weight parameters, and neural networks use class-balanced loss functions

1. Reproducibility: Conda environment isolation, Python 3.11 version locking, explicit dependency package versions
2. Hardware Optimization: TensorFlow Metal plugin leverages Apple Silicon GPU acceleration
3. End-to-End Pipeline: Covers the complete workflow from raw data to business insights, solving the "last mile" problem

This project provides a methodology for building production-grade churn prediction systems:

1. Business objectives drive technical metrics (AUC-ROC ≥0.88 directly links to retention value)
2. Prioritize data quality (sufficient EDA and cleaning lay the foundation for modeling)
3. Systematic workflow (six-stage pipeline reduces risks)
4. Emphasize interpretability (feature importance supports business decisions)
5. Rigorous engineering practices (environment isolation, version control) It is an excellent reference project for data scientists and engineers to build similar systems.