Customer Segmentation Model
This repository contains a trained K-Means clustering model for customer segmentation, along with a scaler to preprocess input features.
Files
kmeans_model.pkl – Trained K-Means modelscaler.pkl – StandardScaler used to normalize features before clustering
Description
The model segments customers into two clusters:
| Cluster | Label | Description |
|---|
| 0 | High Spender | Customers with high sales and profit |
| 1 | Occasional Buyer | Customers with lower or occasional purchases |
The model was trained on the Superstore dataset.
Input Features
To predict the cluster of a customer, the model expects a dictionary with the following keys:
TotalSales – Total sales of the customerAvgSales – Average sales per orderTotalProfit – Total profit from the customerAvgProfit – Average profit per orderTotalQuantity – Total quantity purchasedAvgDiscount – Average discount received (0-1)
