Customer Segmentation & Behavioral Analysis using K-Means Clustering

Developed an unsupervised machine learning model to segment retail and e-commerce customers based on their historical purchasing behavior, frequency of orders, and total monetary spend. This helps businesses target specific customer groups with personalized marketing. Technical Implementation: Cleaned and preprocessed transaction logs using Pandas and NumPy to handle outliers and scale features. Used the Elbow Method and Silhouette Analysis to determine the optimal number of clusters. Applied the K-Means Clustering algorithm via Scikit-Learn to group customers into distinct behavioral profiles (e.g., VIP, Occasional, and At-Risk buyers). Visualized the data distribution and final clusters using Matplotlib and Seaborn to present clear insights to business manager