🛍️ Mall Customer Segmentation using KMeans Clustering

This project applies unsupervised machine learning techniques to segment customers of a mall based on their demographic and behavioral attributes. The goal is to help the mall understand different customer groups and optimize marketing strategies accordingly.

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📊 Project Overview

• Type: Unsupervised Machine Learning
• Algorithm Used: KMeans Clustering
• Tools: Python, scikit-learn, pandas, matplotlib, seaborn, Streamlit (optional)
• Dataset: Mall Customer Dataset

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💡 Objectives

• Identify distinct customer segments based on:
  • Age
  • Annual Income
  • Spending Score
  • Gender (encoded)
• Apply PCA for dimensionality reduction and visualization
• Visualize and interpret the resulting customer clusters

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🧠 Features Used

Feature	Description
Gender	Encoded numerically (0 or 1)
Age	Customer age
Annual Income (k$)	Income in thousands of USD
Spending Score (1-100)	Score assigned by the mall based on spending behavior

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🛠️ Technologies Used

• Python (v3.8+)
• Pandas: Data manipulation
• Scikit-learn: KMeans, PCA, scaling
• Matplotlib / Seaborn: Visualization

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🔍 How It Works

1. Data Preprocessing

   • Handle missing values
   • Encode categorical columns (e.g. gender)
   • Standardize features

2. Choosing Optimal Clusters

   • Elbow Method
   • Silhouette Score

3. KMeans Clustering

   • Fit KMeans(n_clusters=k)
   • Assign each customer to a cluster

4. Dimensionality Reduction

   • Apply PCA to reduce features to 2D
   • Use for visualization only

5. Labeling Clusters

   • Assign segment names based on behavioral interpretation

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▶️ How to Run This Project

📦 1. Clone the Repository

git clone https://github.com/YamenRM/Mall-Customer-Segmentaion.git
cd Mall-Customer-Segmentaion

📥 3. Install Dependencies

• pip install -r requirements.txt

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📊 Visualizations

• Elbow plot for choosing K
• PCA scatter plot with clusters
• Income vs Spending Score by cluster

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💾 Output

The final CSV includes the following columns:

• Age

• Annual Income (k$)

• Spending Score (1-100)

• Cluster

• Segment

• PCA1 / PCA2 (for plotting)

• Saved as: clustered_customers.csv

🔮 Future Improvements

• Try alternative clustering methods: DBSCAN, GMM, Hierarchical

• Include more features (e.g. visit frequency, customer loyalty)

• Deploy app on Streamlit Cloud or HuggingFace Spaces

• Build classifier to predict segment for new customers

🧑‍💻 Author

• YamenRM
• AI Engineering Student | UP
• 📫 yamenrafat132@gmail.com
• 🌍 Gaza, Palestine
• 💪 Stay strong