https://github.com/muhammadfahd/customer_segmentation_using_kmeans
mini machine learning project (unsupervised learning type) that uses K-Means clustering to segment mall customers based on their annual income and spending score. With proper guide and code. Build with Streamlit for UI.
https://github.com/muhammadfahd/customer_segmentation_using_kmeans
huggingface-spaces machine-learning python streamlit unsupervised-clustering
Last synced: about 2 months ago
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mini machine learning project (unsupervised learning type) that uses K-Means clustering to segment mall customers based on their annual income and spending score. With proper guide and code. Build with Streamlit for UI.
- Host: GitHub
- URL: https://github.com/muhammadfahd/customer_segmentation_using_kmeans
- Owner: muhammadfahd
- Created: 2025-05-04T02:07:43.000Z (about 2 months ago)
- Default Branch: main
- Last Pushed: 2025-05-04T02:14:21.000Z (about 2 months ago)
- Last Synced: 2025-05-07T18:13:13.150Z (about 2 months ago)
- Topics: huggingface-spaces, machine-learning, python, streamlit, unsupervised-clustering
- Language: Jupyter Notebook
- Homepage:
- Size: 544 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# 🛍️ Customer Segmentation using K-Means Clustering
This project is a customer segmentation analysis using K-means clustering. The goal is to divide customers into distinct groups based on their annual income and spending score to help businesses make targeted marketing decisions.
This repo contain following
* [Unsuervised Learning K-means Clustering in Detail](./unsupervised%20learning%20kmeans%20clustering.md)
* Implementing k-means clustering using
* [Built from scratch](https://github.com/muhammadfahd/customer_segmentation_using_kmeans/blob/main/k_measn_from_scratch.py)
* [using scikit library](https://github.com/muhammadfahd/customer_segmentation_using_kmeans/blob/main/k_means_using_scikit.py)
* [Building a project with customer segmentation](./customer_Segmentation.ipynb)
* Streamit Lit App [Link](https://huggingface.co/spaces/mfahadkhan/cutsomer_segmentation)
---
## 🧑💻 K-Means Clustering Algorithm
K-means clustering is an unsupervised machine learning algorithm used to partition data into K distinct clusters. The objective of the algorithm is to minimize the within-cluster variance, so the points within each cluster are as similar as possible.
For detailed checkout [here](./unsupervised%20learning%20kmeans%20clustering.md)
## 🧑💻 Implementation
1. **K-Means Clustering from Scratch**
We start by implementing K-means clustering from scratch to understand the internal workings of the algorithm [here](./k_measn_from_scratch.py)
2. **Using Built-in Library (Scikit-Learn)**
To simplify the clustering process, we also use the built-in KMeans algorithm from Scikit-Learn [here](./k_means_using_scikit.py)
---
# Mini Project - Customer Segmenation
In this project, we use K-means clustering to perform customer segmentation. The dataset contains 200 rows and 5 columns representing customer data, including:
* **Annual Income (in thousands)**: Customer's annual income in thousands of dollars.
* **Spending Score (1 to 100)** :A score assigned to customers based on their spending behavior.
* **Age** : Customer’s age.
* **Gender** : Gender of the customer (optional for segmentation).
* **Customer ID** : Unique identifier for each customer.
📊 **Steps Performed**:
* Exploratory Data Analysis (EDA)
* Visualization of clusters
* K-means clustering implementation
* Model Saving for prediction
---
## 🚀 Streamlit UI
We created a Streamlit app to provide an interactive user interface for predicting customer clusters. Users can input their annual income and spending score, and the app will display:
* The predicted cluster
* The cluster meaning (e.g., high spenders, low spenders)
* Visualizations of the cluster distribution
* Recommended actions based on the cluster
---
## 🛠️ Technologies Used
* **Python**: Programming language for implementing machine learning algorithms.
* **Scikit-Learn**: Library for machine learning models (K-means clustering).
* **Streamlit**: Framework to create the interactive web app.
* **Matplotlib**: Library for visualizations.