rasnarim / clustringcasestudy

ClustringCaseStudy

Project Overview

This repository hosts a Jupyter notebook for a Clustering Case Study, specifically focusing on customer segmentation using the K-Means algorithm. The project demonstrates how clustering can be applied to marketing data to identify distinct customer groups and inform targeted marketing strategies.

Notebook Description

The [Clustering Case Study - Customer Segmentation with K-Means - Tier 3.ipynb](Clustering Case Study - Customer Segmentation with K-Means - Tier 3.ipynb) notebook is dedicated to exploring customer segmentation using the K-Means clustering algorithm. The notebook includes the following sections:

1. Introduction: Brief introduction to the concept of clustering and its application in customer segmentation.
2. Data Preprocessing: Techniques employed to prepare the dataset for clustering, such as normalization and handling missing values.
3. Exploratory Data Analysis (EDA): Insights into the dataset through visualization and statistical analysis.
4. K-Means Clustering: Detailed implementation of the K-Means algorithm, including selecting the number of clusters and analyzing the cluster characteristics.
5. Results and Interpretation: Analysis of the clustering results and their implications for customer segmentation and targeted marketing.
6. Conclusions and Future Work: Summary of key findings and suggestions for further research or application in different contexts.