# CustomerSegmentation

**Repository Path**: yang_zi_0709/CustomerSegmentation

## Basic Information

- **Project Name**: CustomerSegmentation
- **Description**: 客户细分分析
- **Primary Language**: Unknown
- **License**: Not specified
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 1
- **Forks**: 0
- **Created**: 2020-02-29
- **Last Updated**: 2021-12-10

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# Customer Segmentation
<img src="https://github.com/MaartenGr/CustomerSegmentation/blob/master/clusters.gif" width="70%"/>

> Code and instructions for techniques to creating, visualizing, and interpreting customer segments.

In this repo, you will find the code and instructions for [this article](https://towardsdatascience.com/cluster-analysis-create-visualize-and-interpret-customer-segments-474e55d00ebb?source=friends_link&sk=822ed9b7a313062378f56182991a1c3b). It is advised to read through the article whilst coding along using the **Customer Segmentation.ipynb** notebook. 

This repo and the corresponding article decribe several techniques for clustering, visualizing and interpreting clustering algorithms and output.
I explored k-Means and DBSCAN as clustering algorithms, t-SNE and PCA for dimensionality reduction, and applied
a variance technique and feature importance to select variables that uniquely represent clusters.