This notebook provides a comprehensive example of how to perform customer segmentation using K-Means clustering, including data preprocessing, visualization, standardization, one-hot encoding, model training, evaluation, and saving/loading the model.

This repository contains a customer segmentation project implemented in a Jupyter Notebook using Python. Customer segmentation is a crucial strategy for businesses aiming to understand their customer base better, enabling targeted marketing strategies and personalized customer experiences.

In this Python notebook, we explore how K-Means can be used for customer segmentation to gain a competitive advantage and improve a business's bottom line.

Python script (and IPython notebook) to perform RFM analysis from customer purchase history data

This notebook provides some skills to perform financial analysis on economical data.

This project used a Kmeans after PCA model to segment retail customers to optimize marketing efforts. When the model repeatedly returned a single cluster, the model was used to prove the customers' homogenous characteristics. Influenced the bank's marketing strategies and initiatives. Developed in Jupyter Notebook with Python for FNB.

IPython notebook to perform RFM analysis from customer purchase history data

Python script (and IPython notebook) to perform RFM analysis from customer purchase history data