Amazon User Segmentation with K-Means Clustering This GitHub repository contains the implementation and analysis of user segmentation for Amazon customers using K-Means clustering. By segmenting customers based on their purchasing behavior and preferences, Amazon can tailor marketing strategies and personalize user experiences to enhance customer satisfaction and drive sales.

Introduction User segmentation is a crucial aspect of customer relationship management, enabling businesses to identify distinct groups of customers with similar characteristics and behaviors. K-Means clustering is a popular unsupervised machine learning algorithm used for segmentation tasks. In this project, we apply K-Means clustering to Amazon user data to segment customers into meaningful groups.

Project Overview This project aims to:

Analyze Amazon user data to understand customer behavior and preferences. Preprocess the data to prepare it for clustering. Apply K-Means clustering to segment customers into distinct groups. Interpret and visualize the clusters to gain insights into customer segments. Develop targeted marketing strategies and personalized recommendations based on the segmentation results. Key Features Data Analysis: Explore and analyze Amazon user data to identify patterns and trends. Data Preprocessing: Clean and preprocess the data to ensure it is suitable for clustering. K-Means Clustering: Implement the K-Means clustering algorithm to segment users into clusters. Cluster Visualization: Visualize the clusters to interpret and understand customer segments.