In this project, we explore and analyze a dataset containing information about credit card users. These users exhibit distinct behaviors and patterns in their spending, payment, and usage of credit facilities. The goal of the project is to employ segmentation techniques, such as clustering models, to categorize customers based on their financial activities. This classification enables financial institutions to gain insights into customer segments, tailor services, and implement targeted strategies for improved customer satisfaction and business outcomes.

Data set link: Customer Segmentation

Languaje: Python.

Libraries: numpy, pandas, matplotlib, seaborn, sklearn.

• Exploratory Data Analysis
• Data Cleaning
• Feature Engineering
• Data enconding
• Models used:
  • DBSCAN Model
  • K-means Clustering Model
  • K-means Clustering Model with PCA