https://github.com/jonad/customer_segment

Creating customer segments using unsupervised machine learning algorithm.
https://github.com/jonad/customer_segment

clustering clustering-algorithm gaussian-mixture-models jupyter-notebook kmeans-clustering matplotlib numpy pandas python scikit-learn

Last synced: about 1 month ago
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Creating customer segments using unsupervised machine learning algorithm.

• Host: GitHub
• URL: https://github.com/jonad/customer_segment
• Owner: jonad
• Created: 2017-01-26T17:13:23.000Z (about 8 years ago)
• Default Branch: master
• Last Pushed: 2017-11-28T21:00:02.000Z (about 7 years ago)
• Last Synced: 2024-11-07T06:28:08.408Z (3 months ago)
• Topics: clustering, clustering-algorithm, gaussian-mixture-models, jupyter-notebook, kmeans-clustering, matplotlib, numpy, pandas, python, scikit-learn
• Language: Jupyter Notebook
• Homepage:
• Size: 989 KB
• Stars: 0
• Watchers: 2
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
## Project: Creating Customer Segments

### Install

This project requires **Python 2.7** and the following Python libraries installed:

- [NumPy](http://www.numpy.org/)

- [Pandas](http://pandas.pydata.org)

- [matplotlib](http://matplotlib.org/)

- [scikit-learn](http://scikit-learn.org/stable/)

- [Jupyter Notebook](http://ipython.org/notebook.html)

If you do not have Python installed yet, it is highly recommended that you install the [Anaconda](http://continuum.io/downloads) distribution of Python, which already has the above packages and more included. Make sure that you select the Python 2.7 installer and not the Python 3.x installer. 

### Run

In a terminal or command window, navigate to the top-level project directory `customer_segments/` (that contains this README) and run one of the following commands:

```bash

ipython notebook customer_segments.ipynb

```  

or

```bash

jupyter notebook customer_segments.ipynb

```

This will open the Jupyter Notebook software and project file in your browser.

## Data

The customer segments data is included as a selection of 440 data points collected on data found from clients of a wholesale distributor in Lisbon, Portugal. More information can be found on the [UCI Machine Learning Repository](https://archive.ics.uci.edu/ml/datasets/Wholesale+customers).

Note (m.u.) is shorthand for *monetary units*.

**Features**

1) `Fresh`: annual spending (m.u.) on fresh products (Continuous); 

2) `Milk`: annual spending (m.u.) on milk products (Continuous); 

3) `Grocery`: annual spending (m.u.) on grocery products (Continuous); 

4) `Frozen`: annual spending (m.u.) on frozen products (Continuous);

5) `Detergents_Paper`: annual spending (m.u.) on detergents and paper products (Continuous);

6) `Delicatessen`: annual spending (m.u.) on and delicatessen products (Continuous); 

7) `Channel`: {Hotel/Restaurant/Cafe - 1, Retail - 2} (Nominal)

8) `Region`: {Lisnon - 1, Oporto - 2, or Other - 3} (Nominal)