This project applies K-Means clustering to segment customers based on demographic and spending behavior.
It applies advanced analytics concepts aligned with the Google Advanced Data Analytics Certificate, including exploratory data analysis, feature scaling, and clustering.
To identify meaningful customer groups that businesses can use for:
- Targeted marketing
- Customer retention
- Personalized offers
- Python
- Pandas, NumPy
- Matplotlib, Seaborn
- Scikit-learn
- Jupyter Notebook
- Load and explore customer data
- Perform exploratory data analysis (EDA) on age, income, and spending behavior
- Scale features using StandardScaler
- Use the Elbow Method to select the optimal number of clusters
- Apply K-Means clustering
- Visualize and interpret customer segments
This scatter plot shows customer segments created using K-Means clustering based on Annual Income and Spending Score, where each color represents a distinct customer group with similar purchasing behavior.
This scatter plot shows customer segments created using K-Means clustering based on Annual Income and Spending Score, where each color represents a distinct customer group with similar purchasing behavior.
Customer-Segmentation-ML/ ├── data/ │ ├── customer_data.csv │ └── customer_data_with_clusters.csv ├── notebooks/ │ └── customer_segmentation.ipynb ├── requirements.txt └── README.md
- Premium customers: High income, high spending
- Budget customers: Low income, low spending
- Careful spenders: High income, low spending
- Deal-driven customers: Low income, high spending
These insights can support targeted campaigns, promotional strategies, and customer engagement initiatives.
Lokesh Yadav
Google Advanced Data Analytics Certified