Telecom Customer Churn Prediction Model
A supervised machine-learning pipeline that analyzes telecom churn drivers and predicts which customer accounts are at risk.
Status:Portfolio project; repository reports 80.6% overall accuracy
View source on GitHub (opens in a new tab)- Services
- Machine Learning
- Stack
- Python, scikit-learn, Pandas, NumPy +2 more
Outcomes
The results
7,043
Customer records analysed
80.6%
Reported overall accuracy
01
Our solution
The project analyzes 7,043 customer records, prepares features through a Scikit-learn pipeline, trains Logistic Regression, and reports overall and churn-class metrics.
02
The outcome
Demonstrates how customer data can support retention prioritization through interpretable predictive modeling.
03
Best for
Telecom, SaaS, subscriptions, retention analytics, and customer-risk modeling.
Technology
The stack behind it
- Backend
- Python
- AI & ML
- scikit-learn
- Logistic Regression
- Data & BI
- Pandas
- NumPy
- Matplotlib