tags:
- machine-learning
- clustering
- k-means
- customer-segmentation
# Customer Segmentation K-Means Model
This repository contains a K-Means clustering model for customer segmentation.
## Model Description
The model segments customers based on three features: Age, Annual Income (k$), and Spending Score (1-100). K-Means clustering was chosen due to its simplicity, efficiency for this type of unsupervised task, and the interpretability of the resulting clusters. The optimal number of clusters was determined using the Elbow Method.
## Training Data
The model was trained on a dataset of **200 customers**, with features: Age, Annual Income (k$), and Spending Score (1-100).
## Usage
To use this model for prediction, you need to provide input in the format:
{"age": <int>, "annual\_income": <float>, "spending\_score": <float>}
## Example
**Input:**
{"age": 30, "annual\_income": 50, "spending\_score": 60}
**Output (example):**
{"cluster\_id": 1}
