Customer Segmentation using Machine Learning

Customer Segmentation using KMeans A machine learning project that applies KMeans clustering on mall customer data to group customers based on Age, Annual Income, and Spending Score. The project helps businesses gain insights into customer behavior, identify high-value groups, and design targeted marketing strategies.

Objective

Analyze customer data to identify patterns and trends Group customers into meaningful segments using clustering algorithms Provide actionable business insights for targeted marketing and decision-making

Dataset

Source: Mall Customers Dataset Features Used: Customer ID Gender Age Annual Income (k$) Spending Score (1–100) Technologies Used Python Pandas, NumPy Matplotlib, Seaborn Scikit-learn Jupyter Notebook

Methodology

1. Data Preprocessing Cleaned and formatted dataset Selected relevant features for clustering Scaled data for better model performance
2. Exploratory Data Analysis (EDA) Analyzed distributions of age, income, and spending Visualized relationships between features Identified key patterns in customer behavior
3. Clustering Techniques Applied K-Means Clustering Used Elbow Method to determine optimal clusters Evaluated clusters using Silhouette Score
4. Model Deployment Trained model saved as kmeans_model.pkl Used for predicting customer segments

Results & Insights

The model identified distinct customer segments such as: High Income – High Spending: Premium customers, target with luxury products High Income – Low Spending: Potential customers, focus on engagement strategies Low Income – High Spending: Offer discounts and deals Low Income – Low Spending: Budget-conscious group These insights can help businesses optimize marketing campaigns and improve customer retention.

Project Structure

├── Customer_Segments.xls ├── Mall_Customers_Clustered.xls ├── Project.ipynb ├── kmeans_model.pkl ├── requirements.txt ├── README.md

Future Improvements

Implement additional clustering algorithms (DBSCAN, Hierarchical Clustering) Build an interactive dashboard using Streamlit Add real-time customer segmentation system Apply dimensionality reduction techniques like PCA Develop recommendation system based on customer segments

Business Impact

Enables personalized marketing strategies Improves customer targeting and engagement Helps increase revenue and customer satisfaction