customer-churn-pipeline

🔄 Customer Churn Pipeline

Predict whether a customer will churn — before it's too late.

customer_churn_pipeline is a machine learning model built to predict customer churn in subscription-based or service businesses. It uses a complete scikit-learn Pipeline that handles preprocessing and classification in a single, production-ready object — making it easy to integrate directly into any Python application.

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📌 Introduction

Customer churn is one of the biggest challenges companies face today. Losing a customer costs far more than retaining one. This model was built to solve that problem — giving businesses the ability to identify at-risk customers before they leave, so proactive action can be taken.

The model is packaged as a .pkl pipeline file, meaning the entire preprocessing and prediction workflow is bundled together. No separate transformation steps needed — just load and predict.

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⚙️ Functionality

The pipeline performs the following steps end-to-end:

Typical input features include:

• Customer tenure (how long they've been a customer)
• Monthly charges / total charges
• Contract type (month-to-month, one year, two year)
• Internet service type
• Payment method
• Support ticket counts, etc.

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🚀 How to Use

1. Install Dependencies

pip install scikit-learn pandas joblib

2. Download the Model

Download customer_churn_pipeline.pkl from this repository.

3. Load and Predict

import joblib
import pandas as pd

# Load the pipeline
pipeline = joblib.load("customer_churn_pipeline.pkl")

# Sample customer data (adjust columns to match your dataset)
sample = pd.DataFrame([{
    "tenure": 12,
    "MonthlyCharges": 65.5,
    "TotalCharges": 786.0,
    "Contract": "Month-to-month",
    "PaymentMethod": "Electronic check",
    "InternetService": "Fiber optic",
    "TechSupport": "No",
    "OnlineSecurity": "No"
}])

# Predict
prediction = pipeline.predict(sample)
print("Churn Prediction:", "Yes" if prediction[0] == 1 else "No")

4. Predict Probability (optional)

proba = pipeline.predict_proba(sample)
print(f"Churn Probability: {proba[0][1] * 100:.2f}%")

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✅ Benefits

• 🔌 Plug-and-Play — The entire preprocessing + prediction pipeline is in one .pkl file. No need to build separate transformers.
• ⚡ Fast Integration — Load and use in just 3 lines of code — ideal for production APIs or dashboards.
• 📊 Interpretable Output — Binary output (0/1) with optional probability scores for nuanced decision-making.
• 🧩 Framework Agnostic — Works with any Python backend: Flask, FastAPI, Streamlit, Django, etc.
• 💼 Business Ready — Designed with real-world churn use cases in mind, enabling marketing and CRM teams to act on predictions immediately.
• 🛡️ Reliable — Built with scikit-learn best practices and reproducible results via a serialized pipeline object.

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📁 Repository Contents

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👤 Author

Asim Taseer 🌐 Website: asimtaseer.unaux.com

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📄 License

This project is licensed under the MIT License — feel free to use, modify, and distribute with attribution.