Customer Segmentation using K-Means Clustering

Project Overview

This project performs customer segmentation for a mall using K-Means clustering, an unsupervised machine learning algorithm.
The goal is to group customers based on Annual Income and Spending Score to help businesses design targeted marketing strategies.

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Dataset

• Source: Mall Customer Dataset
• Features:
  • CustomerID
  • Gender
  • Age
  • Annual Income (k$)
  • Spending Score (1-100)
• Target: None (unsupervised learning problem)

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Workflow

1. Data Loading & Inspection

   • Loaded CSV into a pandas DataFrame.
   • Checked for missing values and data types.

2. Feature Selection

   • Chose Annual Income (k$) and Spending Score (1-100) for clustering.

3. Feature Scaling

   • Standardized features using StandardScaler to improve clustering performance.

4. Finding Optimal Clusters

   • Used the Elbow Method to determine the optimal number of clusters based on WCSS (Within-Cluster Sum of Squares).

5. K-Means Clustering

   • Trained K-Means with the optimal number of clusters.
   • Assigned cluster labels to each customer.

6. Visualization

   • Visualized clusters with different colors.
   • Marked centroids for clarity.

7. Insights

   • Summarized characteristics of each cluster (average income, spending score, etc.).
   • Helps understand customer behavior for targeted marketing.

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Libraries Used

• pandas → Data handling
• numpy → Numerical operations
• matplotlib & seaborn → Visualization
• sklearn → K-Means clustering and scaling

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How to Run

1. Clone the repository:

   git clone <your-repo-url>