nibeditans/crros-churn-prediction-model

CRROS Churn Prediction Model

This model is part of my Customer Retention & Revenue Optimization System (CRROS) project. I've trained this model to predict whether a customer is likely to churn based on their purchasing behavior, engagement history, and engineered customer features.

The goal of the project wasn't just to build a machine learning model, but to simulate a complete business workflow: from realistic customer behavior to feature engineering, predictive modeling, and business decision-making.

What Does This Model Do?

The model predicts the probability of customer churn using customer-level behavioral features.

I've trained this on a synthetic but behavior-driven dataset which was designed to resemble realistic business scenarios rather than completely random data.

The model can be used for:

• Customer churn prediction
• Customer retention analysis
• Machine learning practice
• Model evaluation
• Educational and portfolio projects

Training Data

This model was trained using the CRROS Customer Behavior Dataset, which includes simulated customer profiles, transactions, interactions, and engineered features created specifically for customer analytics and machine learning.

I've designed the dataset with:

• Behavior-driven customer simulation
• Multiple customer segments
• Missing values
• Outliers
• Natural variation and noise

Model Information

• Framework: Scikit-learn
• Task: Binary Classification
• Prediction Target: Customer Churn
• Model Format: Joblib

I've also included the scaler in the repository, that was required during inference to ensure new data is transformed in the same way as the training data.

Notes

This model is intended for educational, learning, and portfolio purposes.

Since it was trained on synthetic data, it should not be used for production business decisions. Its primary purpose is to demonstrate an end-to-end data science workflow using realistic customer behavior simulation.

Resources

If you'd like to explore the complete project or understand how this model was built, you can find everything here:

• GitHub Repository: Customer Retention & Revenue Optimization System
• Medium Project Walkthrough: How to Identify High-Value Customers and Maximize Revenue with Data Science?

Thanks for checking out the model! I hope it helps you learn something new or serves as a useful starting point for your own customer analytics projects.