🔄 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.

📌 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.

⚙️ Functionality

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

Step	Description
Preprocessing	Handles missing values, scales numerical features, and encodes categorical variables
Feature Engineering	Prepares structured tabular customer data for model input
Classification	Predicts churn probability and binary outcome (Churn / No Churn)
Output	Returns a prediction label: `1` (Churn) or `0` (No Churn)

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.

🚀 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}%")

✅ 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.

📁 Repository Contents

File	Description
`customer_churn_pipeline.pkl`	The trained and serialized ML pipeline
`README.md`	Documentation and usage guide

👤 Author

Asim Taseer 🌐 Website: asimtaseer.unaux.com

📄 License

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

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