https://github.com/moustafamohamed01/mall-customer-segmentation-data
Customer segmentation using K-Means clustering based on annual income and spending score.
https://github.com/moustafamohamed01/mall-customer-segmentation-data
data-science data-visualization k-means-clustering machine-learning python scikit-learn unsupervised-learning
Last synced: about 2 months ago
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Customer segmentation using K-Means clustering based on annual income and spending score.
- Host: GitHub
- URL: https://github.com/moustafamohamed01/mall-customer-segmentation-data
- Owner: MoustafaMohamed01
- Created: 2025-03-13T07:53:07.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2025-03-13T08:18:39.000Z (over 1 year ago)
- Last Synced: 2025-03-13T09:23:27.626Z (over 1 year ago)
- Topics: data-science, data-visualization, k-means-clustering, machine-learning, python, scikit-learn, unsupervised-learning
- Language: Jupyter Notebook
- Homepage:
- Size: 141 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
## Overview
This project performs customer segmentation using the K-Means clustering algorithm. It applies unsupervised learning to categorize customers based on their annual income and spending score.
## Dataset
The project uses the **Mall_Customers.csv** dataset, which contains the following features:
- **CustomerID**: Unique ID assigned to each customer.
- **Gender**: Gender of the customer.
- **Age**: Age of the customer.
- **Annual Income (K$)**: Annual income of the customer in thousands.
- **Spending Score (1-100)**: Score assigned by the mall based on customer behavior and spending patterns.
Dataset Source: [Kaggle - Mall Customers Dataset](https://www.kaggle.com/vjchoudhary7/customer-segmentation-tutorial-in-python).
## Project Structure
- **K-Means Clustering**:
- Determines the optimal number of clusters using the Elbow Method.
- Applies K-Means clustering to segment customers.
- Visualizes the clusters with a scatter plot.
## Installation & Setup
### Prerequisites
Ensure you have Python installed along with the required dependencies:
- **Using `pip` directly**:
```sh
pip install numpy pandas seaborn matplotlib scikit-learn
```
- **Using `requirements.txt`** (recommended):
```sh
pip install -r requirements.txt
```
### Running the Script
1. Clone the repository:
```sh
git clone git@github.com:MoustafaMohamed01/Mall-Customer-Segmentation-Data.git
cd Mall-Customer-Segmentation-Data
```
2. Place the **Mall_Customers.csv** file in the same directory as the script.
3. Run the Python script:
```sh
python Customer_Segmentation_using_K-Means_Clustering.py
```
## Methodology
1. **Load Data**: Reads the dataset into a Pandas DataFrame.
2. **Data Preprocessing**: Extracts relevant features (Annual Income and Spending Score).
3. **Finding Optimal Clusters**: Uses the **Elbow Method** to determine the best value of K.
4. **Clustering with K-Means**: Applies K-Means with the optimal K value.
5. **Visualization**: Plots the Elbow Graph and Cluster Scatter Plot.
### **Elbow Method Graph**
The **Elbow Method** helps determine the optimal number of clusters by plotting the Within-Cluster Sum of Squares (WCSS).
### **Customer Segmentation Scatter Plot**
The final K-Means clustering results are visualized in a scatter plot, showing customer segments and their centroids.
## Results & Insights
- The optimal number of clusters (K) is selected using the **Elbow Method**.
- Customers are grouped based on their **Annual Income** and **Spending Score**.
- Business insights can be derived by analyzing these segments (e.g., targeting high-spending customers with promotions).