---
license: mit
tags:
- customer-segmentation
- machine-learning
- clustering
- dbscan
- scikit-learn
---

# Customer Segmentation - DBSCAN

## Overview

This model performs customer segmentation using the DBSCAN clustering algorithm. It identifies groups of customers based on similarities in demographic and behavioral features while also detecting customers that do not belong to any major group.

## Model Type

DBSCAN 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 cluster label to each customer.

Example:

- Cluster 0
- Cluster 1
- Cluster 2
- -1 (Noise / Outlier)

A label of `-1` indicates that the customer was identified as an outlier and does not belong to any major cluster.

## Files Required

- `dbscan_model.joblib`
- `scaler.joblib`

## Usage

```python
import joblib
import numpy as np
scaler = joblib.load("scaler.joblib")
model = joblib.load("dbscan_model.joblib")
customer = np.array([[...]])
customer_scaled = scaler.transform(customer)
# DBSCAN does not support direct prediction on new samples.
# The model is primarily intended for clustering datasets.
labels = model.fit_predict(customer_scaled)
print(labels)
```

## Author

Mithun