Innovation Owner
Mr. BUNYAPONG MUTIWATTANASAWAD
Student
Details
This report details the development of a machine learning model to predict credit card customer churn using real-world financial data. It includes the creation of a web interface and dashboard to provide actionable insights for strategic customer retention.
This report is part of applying the knowledge gained from studying machine learning models and methods for developing a predictive model to identify customers likely to cancel their credit card services with a bank. The project was carried out during an internship at a financial institution, where the creator developed a model to predict customers likely to churn from their credit card services using real customer data through the organization's system. The focus was on building a model that can accurately predict customer churn by selecting features that are appropriate for the prediction model and the unique characteristics of the credit card industry data to ensure the highest possible accuracy and efficiency.
This report also covers the integration of the model into the development of a website, which allows related departments to conveniently use the prediction model. Users can upload data for prediction and receive model results instantly. In addition, a dashboard has been created to present insights from the model's predictions, such as:
- Identifying high-risk customers likely to cancel services
- Providing important analytical information for strategic decision-making
This will help support more efficient marketing planning and customer retention efforts within the organization.
Objective
The objectives include studying and developing machine learning models to predict credit card churn, analyzing data relationships, and utilizing the results to improve service quality and customer retention strategies.
- Study approaches for developing Machine Learning Models from various sources to understand the principles of developing models for predicting the behavior of bank credit card customers.
- Develop a Machine Learning model to predict the churn rate of bank credit card customers.
- Analyze and understand data to identify the importance and hidden relationships within the dataset.
- Test and compare the performance of each Machine Learning model to select the most effective one.
- Utilize the results obtained from the Machine Learning model development to support considerations for improving and developing services to better meet customer needs in the future.