Project: Creating Customer Segments
Content: Unsupervised Learning
Description
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. You’ve been hired by the wholesale distributor to find what types of customers they have to help them make better, more informed business decisions in the future. Your task is to use unsupervised learning techniques to see if any similarities exist between customers, and how to best segment customers into distinct categories.
Install
This project requires Python 2.7 and the following Python libraries installed:
It also need to have software installed to run and execute a Jupyter Notebook
Project Overview
In this project unsupervised learning techniques is applied on product spending data collected for customers of a wholesale distributor in Lisbon, Portugal to identify customer segments hidden in the data.The data is explored by selecting a small subset to sample and determine if any product categories highly correlate with one another. Afterwards, the data is preprocessed by scaling each product category and then identifying (and removing) unwanted outliers. With the good, clean customer spending data,PCA transformations is applied to the data and implement clustering algorithms to segment the transformed customer data. Finally, segmentation found is compared with an additional labeling so that it could assist the wholesale distributor with future service changes.
Project Highlights
This project is designed to get a hands-on experience with unsupervised learning and work towards developing conclusions for a potential client on a real-world dataset. Many companies today collect vast amounts of data on customers and clientele, and have a strong desire to understand the meaningful relationships hidden in their customer base. Being equipped with this information can assist a company engineer future products and services that best satisfy the demands or needs of their customers.
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)