customer-churn-prediction

Churn rate serves as a valuable metric for subscription-based businesses, enabling them to pinpoint dissatisfied customers and uncover weaknesses in products, pricing plans, operations, and customer preferences. This proactive approach helps reduce churn by addressing underlying issues effectively.

Creating a Churn Prediction Workflow involves several key steps within a standardized machine learning project structure:

Defining the Problem and Goal:

Understand the specific insights required from the analysis, addressing stakeholders' concerns and expectations.

Establishing Data Sources:

Identify relevant data sources, such as CRM systems, Analytics services, and customer feedback, essential for the subsequent modeling phase.

Data Preparation, Exploration, and Preprocessing:

Transform raw historical data into a suitable format for machine learning algorithms. This step not only ensures data quality but also enhances overall results.

Modeling and Testing:

Develop and validate churn prediction models using various machine learning algorithms to assess their performance.

Deployment and Monitoring:

Deploy the most suitable model into production, integrating it into existing software or serving as the foundation for a new application. Regular monitoring ensures the model's effectiveness over time.

The deployed application operates through two main use cases:

Online Prediction:

This case generates real-time predictions for individual data points, such as customers, one at a time.

Batch Prediction:

This use involves generating predictions for a batch of observations simultaneously, streamlining the prediction process for multiple data points.