In this project I have implemented unsupervised algorithms to analyze a dataset containing data on various customers' annual spending amounts of diverse product categories for internal structure. One goal of this project is to best describe the variation in the different types of customers that a wholesale distributor interacts with. Doing so would equip the distributor with insight into how to best structure their delivery service to meet the needs of each customer.
This project is from Udacity Machine Learning Engineer Nanodegre.
For more info see creating-customer-segments.ipynb
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.
customer_segments.ipynb: notebook file with coding and explanation about the project.
visuals.py Python file for visualization purposes.
customers.csv Dataset file that include 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 creating-customer-segments/ (that contains this README) and run one of the following commands:
ipython notebook creating-customer-segments.ipynbor
jupyter notebook creating-customer-segments.ipynbThis will open the Jupyter Notebook software and project file in your browser.