crros-purchase-probability-model

CRROS Purchase Probability Model

This model is part of my Customer Retention & Revenue Optimization System (CRROS) project. I've trained this model to estimate the likelihood of a customer making a purchase based on customer behavior, engagement history, and engineered features.

The objective wasn't simply to train another classification model. Instead, this model is one component of a larger end-to-end data science workflow that simulates realistic customer behavior and demonstrates how predictive models can support business decision-making.

What Does This Model Do?

The model predicts the probability that a customer will make a purchase using customer-level behavioral features.

It can be useful for tasks such as:

• Purchase probability prediction
• Customer targeting
• Marketing campaign planning
• Machine learning practice
• Educational and portfolio projects

Within the CRROS project, I later combined this prediction with business rules to support customer targeting and revenue optimization.

Training Data

I've trained this model using the CRROS Customer Behavior Dataset, which contains synthetic but behavior-driven customer data which was designed to simulate realistic business scenarios.

The dataset includes:

• Customer profiles
• Product information
• Purchase history
• Customer interactions
• Engineered customer features

To better resemble real-world data, the simulation also includes missing values, outliers, and natural behavioral variation.

Model Information

• Framework: Scikit-learn
• Task: Binary Classification
• Prediction Target: Purchase Probability
• Model Format: Joblib

This repository includes:

• The trained purchase prediction model
• The fitted scaler used during training
• The preprocessing configuration required before inference

Together, these files provide everything needed to reproduce the same preprocessing pipeline before generating predictions.

Notes

This model was developed for educational and portfolio purposes using synthetic customer data.

Although the data is simulated, the project focuses on building a realistic end-to-end analytics workflow that reflects how customer behavior can be transformed into actionable business insights.

Resources

If you'd like to explore the complete project or understand how this model was developed, you can find more details 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 inspires ideas for your own analytics and machine learning projects.