This project uses the K-means clustering algorithm to segment customers of a retail store based on their purchasing behavior. The goal is to identify different customer groups that can be targeted with tailored marketing strategies. Customer segmentation is a powerful technique that allows businesses to group their customers into distinct clusters, enabling targeted marketing strategies, personalized offers, and improved customer satisfaction.
The dataset used in this project is Mall_Customers.csv, which contains information about customers' annual income, spending score, age, and gender.
The notebook includes the following analysis:
- Exploratory Data Analysis (EDA): Initial visualizations and insights into the dataset.
- K-means Clustering: Implementation of K-means to identify customer segments.
- Cluster Visualization: Visualization of the clusters and their centroids.
- Insights: Business insights and potential strategies based on the clustering results.