This project is part of the "Unsupervised Learning" course and is focused on the segmentation of credit card customers for AllLife Bank. The primary objective is to understand the varying needs of customers and improve targeted marketing strategies. We aim to identify distinct segments among the bank's customers based on their spending behaviors and past interactions with the bank.
The importance of personalized service is ever-growing in the banking industry. Through this project, we will implement clustering algorithms to segment customers, enabling AllLife Bank to tailor their marketing and services according to the specific needs and preferences of each segment.
- Data Exploration: Conduct an in-depth analysis of the customer data, focusing on spending patterns and previous interactions with the bank.
- Customer Segmentation: Apply clustering techniques to segment the customers into distinct groups.
- Strategy Formulation: Provide recommendations on tailored marketing strategies and improved services for each identified customer segment.
- Basic knowledge of clustering algorithms and unsupervised learning.
- An environment capable of running Jupyter Notebooks or Python scripts (e.g., Anaconda).
- Required Python libraries: pandas, numpy, scikit-learn, matplotlib, seaborn.