Skip to content

Using Unsupervised Learning Techniques to Perform Customer Segmentation for a Wholesale Distributor.

Notifications You must be signed in to change notification settings

alifier/Customer_Segments

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 

History

2 Commits
 
 
 
 
 
 

Repository files navigation

Project: Creating Customer Segments

Goal: Using Unsupervised Learning Techniques to Perform Customer Segmentation for a Wholesale Distributor

This report is a modified version of my solution to the 'Creating Customer Segments' Udacity Project that is part of the Machine Learning Engineer Nanodegree program

The report is saved in an iPython Notebook format. To review it click on the Customer_Segmentation.ipynb file.

If you want to run the code in your computer you will need to follow the Install and Run instructions.

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.

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: {Lisbon - 1, Oporto - 2, or Other - 3} (Nominal)

Install

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

You will also need to have software installed to run and execute a Jupyter Notebook

If you do not have Python installed yet, it is highly recommended that you install the Anaconda 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:

ipython notebook customer_segments.ipynb

or

jupyter notebook customer_segments.ipynb

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

About

Using Unsupervised Learning Techniques to Perform Customer Segmentation for a Wholesale Distributor.

Resources

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published