This project applies unsupervised machine learning techniques to segment customers based on purchasing behavior and transactional patterns. The primary aim is to discover meaningful customer groups that can support data-driven marketing, personalization, and business decision-making.
Group customers into distinct segments using machine learning Identify hidden behavioral patterns without labeled data Enable targeted marketing and customer strategy optimization
Customer Segmentation.ipynb– Complete Jupyter Notebook containing data preprocessing, clustering, and visualizationREADME.md– Project overview and usage documentation
Customer-level transactional dataset Contains numerical features such as purchase amount, frequency, or sales metrics Structured data suitable for distance-based clustering algorithms
Python 3.x Jupyter Notebook Pandas for data manipulation NumPy for numerical computation Scikit-learn for preprocessing and clustering Matplotlib and Seaborn for visualization
Load dataset into a Pandas DataFrame Inspect structure, shape, and data types Verify presence of missing or inconsistent values
Select relevant numerical features for clustering Handle missing values if present Apply feature scaling using
StandardScalerto normalize data
Analyze feature distributions Identify outliers and skewness Understand feature relationships and variance
Algorithm used: K-Means Clustering Groups customers by minimizing intra-cluster variance Assigns each customer to the nearest centroid
Use the Elbow Method Train K-Means for multiple values of
kPlot inertia vs number of clusters Identify elbow point indicating optimal cluster count
Train K-Means with selected number of clusters Assign cluster labels to each customer Append cluster labels to original dataset
Scatter plots to visualize cluster separation Cluster-wise comparison of feature means Color-coded plots for interpretability
Analyze centroid values for each cluster Identify high-value, medium-value, and low-value customer groups Translate numerical patterns into business-friendly segments
Targeted marketing campaigns Personalized product recommendations Customer retention strategies Revenue optimization through high-value segments
K-Means assumes spherical clusters Sensitive to feature scaling No external validation metrics implemented Ignores categorical and behavioral context beyond numeric data
Add Silhouette Score and Calinski-Harabasz Index Experiment with DBSCAN and Hierarchical Clustering Apply PCA for dimensionality reduction Perform advanced feature engineering Deploy as a web-based analytics dashboard
Demonstrates practical use of unsupervised learning in business analytics Highlights importance of preprocessing and interpretation Serves as a strong foundation for advanced customer analytics projects
Clone the repository Install required Python libraries Open the notebook in Jupyter Run cells sequentially to reproduce results