WitInMotion/konga-customer-segmentation

Customer segmentation using K-Means clustering and PCA on Konga e-commerce data to uncover actionable business insights. # Customer Segmentation using K-Means Clustering ## Overview This project applies **unsupervised machine learning** to segment customers based on purchasing behavior using Konga e-commerce transaction data. The goal is to uncover meaningful customer groups that can support targeted marketing, improve customer retention, and drive business growth. ## Objectives - Perform exploratory data analysis (EDA) - Engineer meaningful customer-level features - Apply clustering algorithms (K-Means & DBSCAN) - Evaluate clustering performance using metrics - Optimize clustering using PCA - Generate actionable business insights ## Tools & Technologies - Python - Pandas - NumPy - Matplotlib - Scikit-learn ## Dataset The dataset contains transactional data including: - Customer ID - Order ID - Product category - Quantity purchased - Unit price - Total transaction value - Order date - City ## Data Cleaning & EDA - The dataset was checked for missing values — none were found. - Orders were analyzed by city and category, revealing higher activity in major urban areas. - A time-based analysis showed fluctuations in monthly orders, indicating possible seasonal trends. ## Feature Engineering Customer-level features were created to capture behavior: - **Order Count** → number of purchases - **Total Quantity** → total items purchased - **Total Spent** → total revenue per customer - **Recency** → days since last purchase (time-based feature) - **Average Order Value** → average spend per order (ratio feature) - **Orders per Month** → purchase frequency ## Feature Scaling StandardScaler was applied to normalize the dataset, ensuring all features contribute equally during clustering. ## Model Selection ### Elbow Method - Used to determine optimal number of clusters - Suggested **K = 3** ### Silhouette Score - Evaluated cluster quality - Confirmed **K = 3** as optimal ## Models Used ### 1. K-Means Clustering - Segmented customers into 3 groups - Initial sil …