This project involves creating a K-means clustering algorithm to group customers of a retail store based on their purchase history.
The dataset, ‘Mall_Customers.csv’, includes customer details such as Gender, Age, Annual Income, and Spending Score.
Data Selection: Features for clustering were selected from the dataset. Elbow Method: Used to determine the optimal number of clusters. K-Means Clustering: Applied with 5 clusters to segment the customers.
Elbow Plot: Visual representation to show the within-cluster sum of squares (WCSS). Cluster Pairplot: Displays data distribution among the clusters. Box Plots: Illustrate statistical summaries for Annual Income and Spending Score. Cluster Visualization: Scatter plot showing customer groups with cluster centroids.
To run the clustering analysis, import the necessary libraries and follow the steps outlined in the Jupyter Notebook.