🎯 Customer Segmentation Analysis "Know your customers, grow your business!"

🔍 What if you could group customers based on their spending habits and preferences? This project uses machine learning to cluster customers into meaningful segments, helping businesses make data-driven marketing decisions.

📌 Why Customer Segmentation? ✅ Personalized marketing campaigns 🎯 ✅ Improved customer retention ❤️ ✅ Optimized product recommendations 🛍️ ✅ Data-driven business decisions 📊

🚀 Tech Stack & Tools Tool Purpose 🐍 Python Programming Language 📊 Pandas & NumPy Data Manipulation 📉 Matplotlib & Seaborn Data Visualization 🧠 Scikit-learn Machine Learning 📂 Dataset We use the Mall Customers Dataset, which includes: 📌 CustomerID – Unique customer identifier 📌 Gender – Male/Female 📌 Age – Customer's age 📌 Annual Income (k$) – Income in thousands 📌 Spending Score (1-100) – Shopping engagement score

🔎 How It Works ✨ 1. Explore the Data 📊

Check for missing values & outliers Visualize spending trends ✨ 2. Scale & Prepare the Data 🔄

Normalize numerical features Transform categorical data ✨ 3. Apply Clustering Techniques 🤖 ✔ K-Means Clustering – Fast & efficient 🔥 ✔ Hierarchical Clustering – Dendrogram-based grouping 🌲 ✔ DBSCAN – Density-based clustering 🔍

✨ 4. Evaluate & Visualize 🖼️

Elbow Method & Silhouette Score to choose the best clusters Scatter plots & pair plots to visualize segmentation 📊 Results 👥 Customers segmented into X groups based on spending behavior! 🎯 Found high spenders, budget shoppers, and potential VIP customers!