KMeans Clustering Model for Customer Segmentation

Model Description

This model is a KMeans clustering model trained to segment potential vehicle credit customers into different groups based on their responses to a survey. The survey captures various aspects of customer preferences and behaviors regarding vehicle acquisition and financing.

Usage

To use this model, load it with Python's pickle module and input the normalized feature data corresponding to the customer survey responses. The model will assign each new customer to one of the four clusters.

Requirements

Python 3.6+
scikit-learn
numpy
pandas

Example

import pickle
import numpy as np

# Load the model
with open('kmeans_cluster_model.pkl', 'rb') as file:
    model = pickle.load(file)

# Example data (normalized)
data = np.array([[0.1, 0.2, 0.1, -0.1, 0.0, 0.1, -0.2]])
cluster_label = model.predict(data)
print(f'Assigned Cluster: {cluster_label[0]}')

Files

kmeans_cluster_model.pkl: The trained KMeans model.
README.md: This file.
requirements.txt: List of libraries required to run the model.