A wholesale distributor recently tested a change to their delivery method for some customers, by moving from a morning delivery service five days a week to a cheaper evening delivery service three days a week. Initial testing did not discover any significant unsatisfactory results, so they implemented the cheaper option for all customers. Almost immediately, the distributor began getting complaints about the delivery service change and customers were canceling deliveries — losing the distributor more money than what was being saved.
The goal of this project is to find what types of customers the distributors have to help them make better, more informed business decisions in the future. The task is to use unsupervised learning techniques to see if any similarities exist between customers, and how to best segment customers into distinct categories.
An analysis of determining customer segment is found in the report/report.pdf
This project requires Python 2.7 and the following Python libraries installed:
Template code is provided in the notebook customerSegments.ipynb notebook file. Additional supporting code can be found in renders.py.
In a terminal or command window, navigate to the top-level project directory creatingCustomerSegments/ (that contains this README) and run one of the following commands:
jupyter notebook customerSegments.ipynb
The dataset used in this project is included as customers.csv. You can find more information on this dataset on the UCI Machine Learning Repository page.