🎯Mall Customer Segmentation Using K-Means 🚀

Welcome to the Mall Customer Segmentation project! This project leverages the power of K-Means Clustering 🌀 to analyze customer behavior and group them into actionable segments. By doing so, businesses can refine their marketing strategies and boost profitability.

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📋 Table of Contents

• 📖 About the Project
• 🎯 Objectives
• 📊 Dataset Overview
• ⚙️ Methodology
• 🔍 Key Insights
• 📈 Results
• 🚀 Getting Started
• 🤝 Contributing
• 📬 Contact

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📖 About the Project

Customer Segmentation is a critical technique in modern marketing 🛍️. This project uses machine learning to group customers based on their:

• Age 🎂
• Annual Income 💰
• Spending Score 🛒

These clusters help businesses make smarter, data-driven decisions! 💡

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🎯 Objectives

🎯 Identify meaningful customer segments.
🎯 Group customers using the K-Means Clustering algorithm.
🎯 Analyze customer behavior for targeted marketing strategies.

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📊 Dataset Overview

• Source: Mall Customer Segmentation Dataset provided by Exposys Data Labs.
• Features:
  • Gender 👩‍🦱👨‍🦱
  • Age 🎂
  • Annual Income 💰
  • Spending Score 🛒
• Target Attributes: Age, Annual Income, Spending Score.

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⚙️ Methodology

This project follows a structured approach:

1. 📊 Exploratory Data Analysis (EDA):

   • Visualized customer distributions (age, income, and spending score).
   • Observed patterns and trends.

2. 🌀 K-Means Clustering:

   • Applied the K-Means algorithm to group customers.
   • Determined the optimal number of clusters using the Elbow Method 📐.

3. 🎨 Visualization:

   • Created interactive cluster plots to highlight group differences.

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🔍 Key Insights

The customers were grouped into 5 clusters:

1. 🟠 Balanced Customers: Low income, low spending.
2. 🔵 Pinch-Penny Customers: High income, low spending.
3. 🟣 Normal Customers: Average income and spending.
4. 🔴 Spenders: Low income, high spending.
5. 🟢 Target Customers: High income, high spending (prime profit sources 💎).

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📈 Results

✨ Clusters Identified:

• Enabled strategic decision-making for personalized marketing campaigns 🎯.
• Highlighted profitable customer groups and potential improvements in service.

💡 Marketing Recommendations:

• 🎁 Offer exclusive discounts to Target Customers.
• 🌟 Improve services for Pinch-Penny Customers to increase spending.
• 💌 Send weekly promotional emails to Balanced Customers to drive engagement.

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🚀 Getting Started

Prerequisites

Ensure you have the following installed:

• Python 🐍
• Jupyter Notebook 📓

🤝 Contributing

Contributions are always welcome! 🛠️

📬 Contact

For any queries or suggestions, feel free to reach out

🌟 Show Your Support

If you found this project helpful, please give it a ⭐ on GitHub! 😊