mithun-ai/customersegmentation_kmeans

Customer Segmentation - K-Means

Overview

This model performs customer segmentation using the K-Means clustering algorithm. It groups customers with similar demographic and behavioral characteristics into distinct segments that can be used for customer analysis, marketing campaigns, and personalization strategies.

Model Type

K-Means Clustering

Features Used

The model was trained using the following features:

• Gender
• Ever Married
• Age
• Graduated
• Profession
• Work Experience
• Spending Score
• Family Size
• Age Group
• Family Category

Output

The model assigns a customer to a cluster.

Example:

• Cluster 0
• Cluster 1
• Cluster 2
• Cluster 3

Each cluster represents a group of customers with similar characteristics.

Files Required

• kmeans_model.joblib
• scaler.joblib
• pca.joblib

Usage

import joblib
import numpy as np

scaler = joblib.load("scaler.joblib")
pca = joblib.load("pca.joblib")
model = joblib.load("kmeans_model.joblib")
customer = np.array([[...]])
customer_scaled = scaler.transform(customer)
customer_pca = pca.transform(customer_scaled)
cluster = model.predict(customer_pca)
print("Customer Cluster:", cluster[0])

Author

Mithun