by Robert Latimer
The goal of this project is to identify a wholesale distributor's different customer clusters by analyzing data pertaining to customer behaviors. By implementing a PCA algorithm and K-Means Clustering algorithm, we are able to discover distinct groups of customers that display similar behaviors. Based upon the customer's cluster, the distributor is able to cater to each cluster's needs more effectively.
For full detail of the project, please see 'customer_segments_RLatimer_Revised.ipynb'.
This project requires Python 2.7 and the following Python libraries installed:
All orignal coding was completed in the customer_segments_RLatimer_Revised.ipynb iPython Notebook file. Additional supporting code can be found in renders.py.
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 customer_segments_RLatimer_Revised.ipynb
jupyter notebook customer_segments_RLatimer_Revised.ipynb
This will open the iPython Notebook software and project file in your browser.
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.