Customer Churn Prediction

This project implements a machine learning–driven approach to predicting customer churn. We leverage XGBoost, a high-performance gradient boosting algorithm renowned for its accuracy and scalability, to build a robust predictive model.

The provided Jupyter notebook (customer_churn_prediction.ipynb) guides you through the complete workflow—from data exploration and cleaning to model training, fine-tuning, and interpretation.

Running Locally

Prerequisites

• Python 3.11.0 or higher
• pip package manager

Steps

1. Clone the repository:

2. Create and activate a virtual environment:

   python -m venv venv
   source venv/bin/activate 

3. Install the dependencies:

   pip install -r requirements.txt

4. Open and execute customer_churn_prediction.ipynb in your preferred Jupyter environment.

Contact

For more details or questions about this project, feel free to reach out:

• Luis Di Martino – ldimartino@digitalsense.ai
• Nicolás Montes – nmontes@digitalsense.ai

We welcome contributions, feedback, and suggestions.