customer-segmentation

Problem Type: Unsupervised Learning → Clustering : Segment mall customers into distinct groups based on spending behavior.

🧠 Problem Type

Unsupervised Learning – Clustering

📊 Dataset

• Source: Mall Customers Dataset on Kaggle
• Features: Age, Annual Income, Spending Score

📌 Objective

Segment customers into distinct clusters based on income and spending behavior using K-Means.

📈 Workflow

1. Load and preprocess dataset
2. Standardize features
3. Determine optimal k using the elbow method
4. Apply K-Means clustering
5. Visualize clusters
6. Evaluate with Silhouette Score

💡 Results

• Best k: 5 clusters
• Silhouette Score: ~0.55
• Visualized clusters saved in outputs/

📦 Tools & Libraries

Python, Pandas, Scikit-learn, Seaborn, Matplotlib

🙋‍♂️ Author

Your Name