Identify-Customer-Segments

Goal

The goal of the project is to apply unsupervised learning techniques to identify segments of the population that form the core customer base for a mail-order sales company in Germany. These segments can then be used to direct marketing campaigns towards audiences that will have the highest expected rate of return.

Process

• Assess and clean the population demographic data in order to convert it into a usable form.
• Use dimensionality reduction techniques (mainly PCA) to help surface the main signals and associations in the data.
• Organize the general population into clusters.
• Apply the techniques and models that were fit on the demographic data to the customers data.
• Compare the distribution of people by cluster for the customer data to that of the general population to identify the clusters of the population that should be targeted.

Results

The analysis provided the following information about the popular demographic for the company:

• They are males (ANREDE_KZ = 1)
• living in micocells in PLZ8 with lower share of 6-10 family home (PLZ8_ANTG3 = 1)
• and no 10+ family homes (PLZ8_ANTG4 = 0)
• with estimated age 46+ years old (ALTERSKATEGORIE_GROB 3 and 4)
• with very high FINANZ_MINIMALIST (FINANZ_MINIMALIST = 5)
• with very high FINANZ_SPARER (FINANZ_SPARER = 1)
• with average affinity for SEMIO_TRADV and SEMIO_REL (SEMIO_TRADV and SEMIO_REL = 4)
• and high affinity for SEMIO_KAEM (SEMIO_KAEM = 3)

Install

This project requires Python 3.x and the following Python libraries installed:

• NumPy
• Pandas
• matplotlib
• scikit-learn
• Seaborn

You will also need to have software installed to run and execute an iPython Notebook

Code

Code is provided in the Identify_Customer_Segments.ipynb notebook file.

Run

In a terminal or command window, navigate to the top-level project directory (that contains this README) and run one of the following commands:

ipython notebook Identify_Customer_Segments.ipynb

or

jupyter notebook Identify_Customer_Segments.ipynb

This will open the iPython Notebook software and project file in your browser.

Data

The dataset consists of two files:

• Udacity_AZDIAS_Subset.csv
• Udacity_CUSTOMERS_Subset.csv

Features

• Features are defined in the Data_Dictionary.md file and AZDIAS_Feature_Summary.csv file.