https://github.com/anastasius21/customersegmentation
Customer Segmentation Model using KMeans Clustering
https://github.com/anastasius21/customersegmentation
customer-segmentation data-science elbow-method kmeans-clustering machine-learning python wcss
Last synced: about 2 months ago
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Customer Segmentation Model using KMeans Clustering
- Host: GitHub
- URL: https://github.com/anastasius21/customersegmentation
- Owner: anastasius21
- Created: 2025-04-15T12:28:54.000Z (about 1 year ago)
- Default Branch: main
- Last Pushed: 2025-04-15T12:39:50.000Z (about 1 year ago)
- Last Synced: 2025-04-15T13:34:25.864Z (about 1 year ago)
- Topics: customer-segmentation, data-science, elbow-method, kmeans-clustering, machine-learning, python, wcss
- Language: Jupyter Notebook
- Homepage:
- Size: 71.3 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# Customer Segmentation
Customer Segmentation Model using KMeans Clustering on the dataset taken from kaggle (included in the repo).
# Project Overview
Companies and Organizations sometimes need to group customers based on the similarity among them. In this dataset, we had customers Annual Income and Spending Score, which we pushed in a dataframe X. Then to find the optimal number of clusters we used the Elbow Method. Using the WCSS, we found out 5 to be the appropriate number of clusters. Then, the KMeans Clustering Model was trained and then the model was saved which was later integrated into a basic streamlit app.
# Libraries Used
Pandas
Scikit Learn
Joblib
Matpotlib
# Repository Strcutre
StreamLit App
Dataset
Jupyter Notebook