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https://github.com/nneji123/customer-segmentation-dash-application
A Customer Segmentation Application using Kmeans Clustering built with Dash
https://github.com/nneji123/customer-segmentation-dash-application
cd ci dash deployment docker flask git github-actions heroku kmeans kmeans-clustering python sklearn
Last synced: about 2 months ago
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A Customer Segmentation Application using Kmeans Clustering built with Dash
- Host: GitHub
- URL: https://github.com/nneji123/customer-segmentation-dash-application
- Owner: Nneji123
- License: mit
- Created: 2022-07-18T09:46:41.000Z (over 2 years ago)
- Default Branch: main
- Last Pushed: 2022-07-19T10:28:29.000Z (over 2 years ago)
- Last Synced: 2023-03-04T01:49:25.200Z (almost 2 years ago)
- Topics: cd, ci, dash, deployment, docker, flask, git, github-actions, heroku, kmeans, kmeans-clustering, python, sklearn
- Language: Python
- Homepage: https://custsegapp.herokuapp.com/
- Size: 2.47 MB
- Stars: 4
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# Customer-Segmentation-Dash-App
A Customer Segmentation Application using Kmeans Clustering built with Dash
[](https://www.python.org)
[](http://www.pytorch.org/news.html)
[](https://custsegapp.herokuapp.com/docs)
## Problem Statement
In this project, I worked with data from the Survey of Consumer Finances (SCF). The SCF is a survey sponsored by the US Federal Reserve. It tracks financial, demographic, and opinion information about families in the United States. The survey is conducted every three years, and we'll work with an extract of the results from 2019.
**This Dash App utilizes a Machine Learning model built with KMeans to segment customers based on various features**
The App can be can be viewed [here](https://custsegapp.herokuapp.com)
## Data Preparation
*From the US Federal Reserve website:*
The Survey of Consumer Finances (SCF) is normally a triennial cross-sectional survey of U.S. families. The survey data include information on families’ balance sheets, pensions, income, and demographic characteristics. Information is also included from related surveys of pension providers and the earlier such surveys conducted by the Federal Reserve Board. No other study for the country collects comparable information. Data from the SCF are widely used, from analysis at the Federal Reserve and other branches of government to scholarly work at the major economic research centers.
[Dataset Link](https://www.federalreserve.gov/econres/scfindex.htm)
## Project Outline
1. Compare characteristics across subgroups using a side-by-side bar chart.
2. Build a k-means clustering model.
3. Conduct feature selection for clustering based on variance.
4. Reduce high-dimensional data using principal component analysis (PCA).
5. Design, build and deploy a Dash web application.
## Preview
### Dash App Demo
## 💻 Deploying the Application to Heroku
Assuming you have git and heroku cli installed just carry out the following steps:
1. Clone the repository
```
git clone https://github.com/Nneji123/Customer-Segmentation-Dash-Application.git
```
2. Change the working directory
```
cd Customer-Segmentation-Dash-Application
```
3. Login to Heroku
```
heroku login
heroku container:login
```
4. Create your application
```
heroku create your-app-name
```
Replace **your-app-name** with the name of your choosing.
5. Build the image and push to Container Registry:
```
heroku container:push web
```
6. Then release the image to your app:
```
heroku container:release web
```