In this project I will 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. I will first explore the data by selecting a small subset to sample and determine if any product categories highly correlate with one another. Afterwards, I will preprocess the data by scaling each product category and then identifying (and removing) unwanted outliers. With the good, clean customer spending data, you will apply PCA transformations to the data and implement clustering algorithms to segment the transformed customer data. Finally, I will compare the segmentation found with an additional labeling and consider ways this information could assist the wholesale distributor with future service changes.
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
If you do not have Python installed yet, it is highly recommended that you install the Anaconda distribution of Python, which already has the above packages and more included. Make sure that you select the Python 2.7 installer and not the Python 3.x installer.
The contents of this repository are covered under the MIT License.