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.

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:

  pip install numpy pandas seaborn matplotlib scikit-learn

• Using requirements.txt (recommended):

  pip install -r requirements.txt

Running the Script

1. Clone the repository:

   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:

   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).