/CustomerSegments

Clustering Algorithm to Create Customer Segments

Primary LanguageJupyter Notebook

Content: Unsupervised Learning

Project: Creating Customer Segments

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

Code

The analysis is performed in the customer_segments.ipynb notebook file. In a terminal, enter:

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.

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)