Creating Customer Segments using principal component analysis (PCA) and K-means clustering of unsupervised machine learning
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 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: {Lisnon - 1, Oporto - 2, or Other - 3} (Nominal)