🛍️ Customer Segmentation using K-Means Clustering

Segmenting mall customers based on their Annual Income and Spending Score using the K-Means clustering algorithm.

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📌 Project Objective

To perform unsupervised customer segmentation to help businesses understand different customer types and enable targeted marketing strategies.

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📊 Dataset

• Source: Mall_Customers.csv
• Features:
  • CustomerID
  • Gender
  • Age
  • Annual Income (k$)
  • Spending Score (1-100)

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⚙️ Technologies & Tools Used

• Python 🐍
• Pandas
• NumPy
• Matplotlib
• Seaborn
• Scikit-learn (KMeans Clustering)

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🚀 Workflow

1. Data Loading and Cleaning

   • Loaded dataset using pandas
   • Checked for missing values

2. Feature Selection

   • Used only Annual Income and Spending Score for clustering

3. Elbow Method for Optimal Clusters

   • Calculated WCSS for K=1 to 10
   • Plotted Elbow graph to find best K (optimal at K=5)

4. K-Means Clustering

   • Applied KMeans(n_clusters=5)
   • Clustered customers into 5 segments

5. Visualization

   • Plotted each cluster with distinct color
   • Marked centroids of clusters

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📈 Visual Output

Customer Clusters	Elbow Method

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📌 Insights

• Cluster 1: High income, low spenders
• Cluster 2: Low income, high spenders
• Cluster 3: Average income, average spenders
• ...
• Business can use this for targeted advertising, loyalty programs, and personalized offers

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📁 How to Run

git clone https://github.com/yourusername/customer-segmentation-kmeans.git
cd customer-segmentation-kmeans
pip install -r requirements.txt
jupyter notebook