Customer segmentation plays a vital role in understanding customer behavior and improving business decision-making in the retail industry. This project presents the implementation of customer segmentation using RFM (Recency, Frequency, Monetary) Analysis and K-Means Clustering on an online retail transactional dataset. The dataset contains transactions of a UK-based non-store online retail company collected between 01/12/2010 and 09/12/2011. The primary objective of the system is to identify valuable customer groups based on their purchasing patterns and generate graphical representations for better business insights.The proposed system first preprocesses the dataset by removing missing and irrelevant values and then calculates RFM parameters for each customer. Min-Max Scaling is applied to normalize the data before clustering. The K-Means clustering algorithm is used to divide customers into multiple groups based on similarities in recency, purchase frequency, and monetary spending. The optimal number of clusters is identified using the Elbow Method and Silhouette Analysis techniques.