9tribez17 / qf_group12_churn_prediction Goto Github PK

This is QF5214 project of GROUP 12 Bank Customer Churn Prediction

1. Problem Stataement

In the dynamic landscape of the financial sector, a critical challenge faced by banks is the retention of their clientele. The ability to accurately predict which customers are at risk of discontinuing their banking services, referred to as customer churn, can significantly impact a bank’s sustainability and growth. This report focuses on develop- ing a predictive model that leverages customer data to forecast the likelihood of clients either continuing with or terminating their financial services. By identifying potential churners, banks can implement targeted retention strategies, thus mitigating financial losses and bolstering customer loyalty.

2. Dataset

We select two bank customer churn datasets: dataset 1, dataset 2, which contains over one hundred and fifty thousand structured and unstructured data of bank customers who either left the bank or continue to be a customer. You can also download the datasets in the data file.

3. Architecture Design

This is our design of our architecture. You can find the cleaned dataset through ELT process in data file. This image is a good guidence if you want to follow our step throughout our code.

4. Demo

We also provide a demo in demo file. In the demo, you can see a csv dataset for testing, a pth trained model, and an ipynb. You can follow the code in the jupyter notebook to predict the bank customer churn. Or you can visit online demo in Google colab.