This project applies Unsupervised Machine Learning algorithms to real-life data from a wholesale retailer's customer purchase data to discover customer segments. We were able to split the customers into 2 major segments, which detailed their purchasing habits and trends. Using these segments, we were able to help the wholesale retailer devise a A/B Testing plan to test changes in delivery schedules.
These instructions will get you a copy of the project up and running on your local machine for development and testing purposes. See deployment for notes on how to deploy the project on a live system.
- Python 2.7
- NumPy
- Pandas
- IPython
- matplotlib
- sklearn
A step by step series of examples that tell you have to get a development env running
- Install Python 2.7 environment with needed packages listed in Prerequistes
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.
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.ipynbor
jupyter notebook customer_segments.ipynbThis will open the Jupyter Notebook software and project file in your browser.