🛒 Customer Segmentation Using K-Means Clustering

📌 Objective

This project applies K-Means clustering to segment customers based on their Annual Income and Spending Score. The goal is to identify different groups of shoppers, which businesses can use for targeted marketing strategies.

📊 Dataset

• Source: Kaggle - Customer Segmentation Dataset
• Columns Used:
  • Annual Income (k$): Customer’s yearly income in thousand dollars.
  • Spending Score (1-100): Score assigned based on shopping behavior.

🛠️ Steps

1. Load the Dataset

• Read the dataset using Pandas.
• Explore basic statistics and check for missing values.

2. Data Preprocessing

• No missing values were found.
• Selected Annual Income and Spending Score for clustering.

3. Exploratory Data Analysis

📊 Distribution of Annual Income

📊 Distribution of Spending Score

📊 Annual Income vs Spending Score

4. Finding Optimal Clusters (Elbow Method)

• Used the Elbow Method to determine the best number of clusters.
• Plotted inertia values to identify the optimal k.

📈 Elbow Method Plot

5. Apply K-Means Clustering

• Applied K-Means with k=5.
• Assigned each customer a cluster label.

6. Visualizing the Clusters

• Scatter plot to illustrate different customer segments.

🎨 Customer Segments

🔍 Findings

• Cluster 1: Low income, low spending (budget-conscious shoppers).
• Cluster 2: High income, high spending (luxury shoppers).
• Cluster 3: Average income, average spending.
• Cluster 4: High income, low spending (careful spenders).
• Cluster 5: Low income, high spending (impulsive shoppers).

📂 Files in Repository

• Mall_Customers.csv: Original dataset.
• customer_segments.csv: Processed dataset with assigned clusters.
• customer_segmentation.ipynb: Jupyter Notebook with all code.
• README.md: Project overview.
• annual_income_distribution.png, spending_score_distribution.png, income_vs_spending.png: Distribution plots.
• elbow_method.png, customer_segments.png: Visualizations of clustering results.

🚀 Next Steps

• Try different clustering algorithms like DBSCAN or Hierarchical Clustering.
• Add age and gender to the clustering model for deeper insights.
• Build a dashboard to visualize customer segments interactively.

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Author: Andrew Jaya Satyo
LinkedIn: linkedin.com/in/andrew-jaya-satyo-1501992b4
Email: andrewjaya12345@gmail.com