Mall Customer Segmentation using Machine Learning 📌 Project Overview
This project performs customer segmentation for a mall's dataset using unsupervised machine learning techniques. The goal is to group customers into distinct clusters based on demographic and spending behavior so that marketing strategies can be tailored for each segment.
Key Objectives: Understand customer demographics and spending habits. Apply clustering algorithms to identify distinct customer groups. Provide actionable insights for targeted marketing.
📂 Dataset Dataset Name: Mall_Customers.csv
Attributes: CustomerID – Unique ID for each customer Gender – Male/Female Age – Age of the customer Annual Income (k$) – Annual income in thousand dollars Spending Score (1–100) – Score assigned by the mall based on customer spending behavior and loyalty Source: Available on Kaggle and other open data repositories.
🛠 Tools & Technologies Language: Python Libraries: Pandas, NumPy, Matplotlib, Seaborn, Scikit-learn Algorithm: K-Means Clustering (also tried Hierarchical Clustering for comparison)
📊 Exploratory Data Analysis (EDA) The EDA phase includes: Checking missing values and data types Gender distribution analysis Age distribution visualization Annual income & spending score patterns Relationship between features using scatter plots
⚙️ Methodology Data Preprocessing Removed unnecessary columns (CustomerID for clustering) Handled categorical variables (Gender → numeric encoding) Feature scaling (if required) Determining Optimal Clusters Used Elbow Method and Silhouette Score to decide the best number of clusters. Model Training Applied K-Means Clustering on selected features. Visualized clusters with color-coded scatter plots. Insights & Recommendations Identified distinct customer segments (e.g., high income–high spending, low income–low spending, etc.)
📈 Results Found optimal clusters (e.g., 5 clusters based on spending habits and income) Clear separation between customer groups, aiding in targeted marketing.
📷 Visualizations Age distribution Income vs. Spending score scatter plots Cluster visualization after applying K-Means
🚀 How to Run Clone the repository: git clone https://github.com/yourusername/mall-customer-segmentation.git Navigate to the project folder: cd mall-customer-segmentation
Install dependencies: pip install -r requirements.txt Run the Jupyter Notebook: jupyter notebook
📌 Future Improvements Implement other clustering techniques (DBSCAN, Gaussian Mixture Models) Add more customer features (e.g., purchase history, location) Build an interactive dashboard for segmentation insights