Creating Customer Segments Project for Udacity Machine Learning Engineer Nanodegree

Project overview

This is Creating Customer Segments project for Udacity Machine Learning Engineer Nanodegree. In this project, I apply unsupervised learning techniques on product spending data collected for customers of a wholesale distributor in Lisbon, Portugal to identify customer segments hidden in the data.

• First, I explore the data by selecting a small subset to sample and determine if any product categories highly correlate with one another.
• Then, I preprocess the data by scaling each product category and then identifying (and removing) unwanted outliers.
• Afterwards, I apply Principle Component Analysis (PCA) transformations to the data and implement Gaussian Mixture Model (GMM) clustering algorithm to segment the transformed customer data using Silhouette coefficient to choose the number of clusters.
• Finally, I compare the segmentation found with an additional labeling and consider ways this information could assist the wholesale distributor with future service changes.

Prerequisites

The code is Python in a Jupyter Notebook and it uses:

• SciKit-learn
• Pandas
• Matplotlib
• NumPy

You can install everything you need to run this project with Anaconda.

Installing

Clone this repository:

git clone https://github.com/rauf-mifteev/MLND_Customer_Segments.git

Run the Code

Navigate to the cloned directories location and start jupyter notebook with costomer_segments.ipynb:

jupyter notebook customer_segments.ipynb