jesuisnicolasdavid / k-means-clustering

Creating Customer Segments

Unsupervised Learning Project

Template code

In this directory (customer_segments/), run ipython notebook, open customer_segments.ipynb and follow the instructions.

Note: You need Python 2.7, NumPy, pandas, matplotlib and scikit-learn to work on this notebook.

Dataset

The dataset refers to clients of a wholesale distributor. It includes the annual spending in monetary units (m.u.) on diverse product categories.

It is part of a larger database published with the following paper:

Abreu, N. (2011). Analise do perfil do cliente Recheio e desenvolvimento de um sistema promocional. Mestrado em Marketing, ISCTE-IUL, Lisbon.

Attributes

• Fresh: annual spending (m.u.) on fresh products (Continuous)
• Milk: annual spending (m.u.) on milk products (Continuous)
• Grocery: annual spending (m.u.)on grocery products (Continuous)
• Frozen: annual spending (m.u.)on frozen products (Continuous)
• Detergents_Paper: annual spending (m.u.) on detergents and paper products (Continuous)
• Delicatessen: annual spending (m.u.)on and delicatessen products (Continuous)

Descriptive statistics

Attribute: (Minimum, Maximum, Mean, Std. Deviation)

• Fresh: ( 3, 112151, 12000.30, 12647.329)
• Milk: (55, 73498, 5796.27, 7380.377)
• Grocery: (3, 92780, 7951.28, 9503.163)
• Frozen: (25, 60869, 3071.93, 4854.673)
• Detergents_Paper: (3, 40827, 2881.49, 4767.854)
• Delicatessen: (3, 47943, 1524.87, 2820.106)