I Built a Churn Prediction Model for a Telecom Company. Here’s What the Data Actually Revealed.

2026-06-20 · Source: Machine Learning on Medium · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Data Science & Analytics · Depth: Intermediate, short

Summary

A churn prediction model for a telecom company was developed using IBM's Telco Customer Churn dataset, comprising 7,043 customer profiles with 21 features. The project details an end-to-end machine learning pipeline, from handling messy CSV data, specifically coercing "TotalCharges" from string to float and dropping 11 rows with whitespace, to generating a ranked list of high-risk accounts. Exploratory Data Analysis (EDA) revealed key churn predictors: month-to-month contract types, fiber optic internet service (despite being premium), and short customer tenure, particularly when combined with high monthly charges in early tenure. The ML pipeline involved encoding categorical variables, scaling continuous features, and an 80/20 stratified train-test split. Comparing Logistic Regression and Random Forest Classifier, the Random Forest model outperformed on all metrics, with tenure, monthly charges, and total charges identified as top predictors.

Key takeaway

For data scientists building churn prediction models, prioritize comprehensive Exploratory Data Analysis before modeling to uncover critical feature relationships. Your model comparison should focus on metrics like recall, as minimizing false negatives is crucial for retention efforts. Use the model's prioritized output to direct retention teams towards the highest-risk customers, enabling targeted interventions like discounts or service upgrades to improve customer lifetime value.

Key insights

Effective churn prediction requires thorough EDA and model comparison to reveal non-linear customer behavior.

Principles

• Contract type strongly predicts churn.
• Early tenure with high charges is high risk.
• Recall is critical for churn models.

Method

The ML pipeline involves encoding, scaling, 80/20 stratified split, then comparing Logistic Regression and Random Forest, prioritizing recall.

In practice

• Prioritize retention efforts on high-churn-risk customers.
• Offer discounts or upgrades to at-risk new customers.

Topics

• Churn Prediction
• Machine Learning Pipeline
• Exploratory Data Analysis
• Random Forest Classifier
• Customer Retention
• Telecom Industry

Code references

• datastorylabs/TelecoNet-Python-ML-Project

Best for: Machine Learning Engineer, Data Scientist, AI Student

Related on AIssential

• Extra #12 - Turning Churn Predictions into Business Actions — Effective churn prediction requires economic threshold optimization and comprehensive model diagnostics beyond basic metrics.
• When Customers Churn at Renewal: Was It the Price or the Project? — Accurate churn attribution at renewal requires distinguishing between price shock, value exhaustion, and their interaction.
• DIY #22 - Build a Churn Detection Model from Scratch — Customer churn prediction is a binary classification problem solvable with behavioral data and machine learning.
• Building AI-Ready Subscription Analytics: How to Go From Dashboards to Decisions — AI-ready subscription analytics transforms historical data into predictive, actionable insights for business growth.
• I Built a Demand Forecasting Model at a Real FMCG Company. It Failed First. (Part 1) — Segmenting diverse demand patterns before forecasting significantly improves model accuracy.
• ChurnNet: A Optimized Modern AI for Churn Prediction — Traditional ML models often outperform complex time series AI for churn prediction in performance, efficiency, and resources.

Open in AIssential →

Editorial summary, takeaway, and curation by AIssential. Original article published by Machine Learning on Medium.