Unsupervised Learning

Creating Customer Segments

Unsupervised learning techniques are used to see if any similarities exist between wholesale customers and how to best segment customers into distinct categories.

Install

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

• NumPy
• Pandas
• matplotlib
• scikit-learn

This project is executed in a Jupyter Notebook

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.

Code

Template code is provided in the customer_segments.ipynb notebook file. The visuals.py Python file and the housing.csv dataset file will be requiered to complete the project.

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 the browser. The contents can be executed by using Shift-Enter.

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