Customer Churn Prediction

Abstract

1. Introduction

A complete and straightforward machine learning pipeline to predict customer churn using a Random Forest Classifier.

The primary objective of this project is to build a predictive model that can identify customers who are likely to stop using a service (churn). By accurately predicting churn, businesses can proactively take measures to retain valuable customers. This repository demonstrates a full, albeit simple, end-to-end pipeline from training to model serialization.

2. Objective

This project provides an end-to-end machine learning pipeline for predicting customer churn. Using Python and the scikit-learn library, it implements a Random Forest Classifier to train a model on a synthetic dataset.

This project demonstrates how Python can be applied to a real-world Data Science problem.

3. Key Features / Modules

• Clean, Synthetic Dataset: Includes a sample churn_sample.csv for easy and immediate training.
• Model Training: A clear and commented Python script (train/train_churn.py) that handles data loading, training, and saving.
• Random Forest Classifier: Implements a powerful and popular ensemble learning method for robust classification.
• Model Serialization: The trained model is saved as churn_model.pkl, ready for deployment or inference.
• Minimal Dependencies: The requirements.txt file makes setup quick and easy.

4. Technology Stack

• Language: Python
• Core Libraries:
• scikit-learn (for the machine learning model)
• pandas (for data manipulation)

5. System Requirements

General requirements for this technology stack — check the README for exact versions.

• Python 3.8 or later
• pip / virtualenv for dependencies
• VS Code, PyCharm or Jupyter Notebook
• Git (to clone the repository)

6. Installation & Setup

git clone https://github.com/rahuloraj/Customer-Churn-Prediction.git
cd Customer-Churn-Prediction

1. Clone the repository:
2. Create a virtual environment (recommended):
3. Install the required dependencies:
4. Run the training script:
5. Check the output:

git clone [https://github.com/your-username/customer-churn-prediction.git](https://github.com/your-username/customer-churn-prediction.git)
    cd customer-churn-prediction

python -m venv venv
    source venv/bin/activate  # On Windows, use `venv\Scripts\activate`

pip install -r requirements.txt

python train/train_churn.py

Full setup instructions are in the project README.

7. Future Enhancements

Suggested extensions you can add to make this your own project.

• Turn the analysis into an interactive dashboard
• Automate data refresh with a scheduled job
• Add a predictive model on top of the analysis

8. Viva / Review Questions

Common questions examiners ask for projects in this domain.

1. What is the source of the dataset and how was missing data handled?
2. Which exploratory analysis steps revealed the most useful insight?
3. Why were these particular charts chosen to present the data?
4. Which statistical or ML technique supports the conclusions?
5. How could the analysis be automated or refreshed with new data?

9. Source Code & License

This project is developed by rahuloraj and published on GitHub under the MIT License. Please follow the license terms and credit the original author when you use or modify this code.