This project is an example of customer segmentation for a credit card company using KMeans clustering algorithm. The goal of this project is to cluster customers based on their credit card usage behavior and identify groups with similar characteristics, which can be used for targeted marketing campaigns and personalized product offerings.
The following libraries are required to run this project:
numpy pandas plotly.express scikit-learn You can install them using pip:
bash
pip install numpy pandas plotly.express scikit-learn
CC GENERAL.csv: This file contains the raw data downloaded from Kaggle.
customer_segmentation.ipynb: This file contains the Jupyter notebooks used for data cleaning, exploratory data analysis, and model building.
data_dictionary.md: This file contains description of features.
README.md: This file.
The KMeans clustering algorithm will identify groups of customers with similar credit card usage behavior. The number of clusters is a hyperparameter that can be tuned to optimize the clustering performance.
After clustering, the credit card company can use the cluster labels to segment customers and develop targeted marketing campaigns and personalized product offerings for each group.