E-commerce-Customer-Segmentation

This project aims to perform customer segmentation analysis on an E-commerce dataset using the K-means clustering algorithm. The goal is to identify the optimal number of clusters (k) based on the maximum silhouette score and uncover purchasing patterns among customers.

Dataset Information:

The data was collected from a well known E-commerce website over a period of time based on the customer’s search profile.

Variable Description:
Column               Description
Cust_ID              Unique numbering for customers
Gender               Gender of the customer
Orders               Number of orders placed by each customer in the past

Remaining 35 features (brands) contains the number of times
customers have searched them

PERCENTAGE OF GENDER

NUMBER OF ORDERS PLACED BY CUSTOMERS

OVERALL ORDERS PLACED BY CUSTOMERS FROM 0 TO 12

SILHOUTTE VISUALIZER:

PLOTTING OPTIMAL NUMBER OF CLUSTER(K):

CLUSTER ANALYSIS:

CONCLUSION:

In E-commerce customer segmentation the k-Means clustering plays major role for identifying the purchase pattern based on customer interest ,Here I used silhoutte score for finding an optimal cluster number to predict the right n_cluster. After that I split the two types cluster 1 is previlge to moderate customer and cluster 0 is low-set of interest in purchasing